Fevers. Boogers. Yucky meds. IDEO Play Lab designers dove deeply into the challenges of sick days and doctors’ appointments through video diaries, online surveys, and in-person interviews with parents, healthcare providers, and small children in homes and pediatric offices across the US. They heard about the struggles of giving medicines, wonky thermometer readings, kids having “stethoscope scaries,” and, worst of all, what happens when kiddos hear the “s-word”—shot.

‍‍The research confirmed Sydney and Kaitlin’s initial hunches about playful healthcare products and provided the team with insights on how to move forward. The answer was a formula of sorts. To help reduce kids’ anxiety and keep them calm at the doctor, give them a choice in their care whenever possible. Build familiarity with doctor’s visits and medical devices through playful moments at home. Redirect their attention away from fear. Design for multisensory richness. And provide age-appropriate narratives about healthcare experiences.

Inspired by this kid-centric formula, the IDEO Play Lab team worked closely with Sydney, Kaitlin, and their co-founder, Michael Kamins, to iterate on several designs, winnowing down more than 150 brainstorm ideas into a dozen tangible prototypes (or tinker models) that were shown to doctors, nurses, parents, and young kids for feedback. The designs—a rocket ship medicine dispenser, an elephant-shaped nose aspirator, and a narwhal nasal bulb, among others—were intended to evoke a sense of playfulness, comfort, and familiarity, with features designed for maximum cuteness. Some even lit up and played sounds, like popular toys.

While the designs passed the fun test with kids, they also needed to be functional for parents, trusted by doctors, easily manufactured, and affordable. Eventually, they would need to be approved by the FDA as regulated medical devices. Playhouse MD also requested patentable innovations to help protect their products from imitation.

With these constraints in mind, IDEO Play Lab delivered a collection of nine unique, high-fidelity prototypes that add a dose of fun to caretaking challenges in homes and doctors’ offices. The final designs were durable, medically sanitizable, and safe for testing with real kids, which helped the startup secure multiple rounds of funding and bring its first products to market in 18 months.

Playhouse MD began selling its first four direct-to-consumer products on its website in June 2025: two Medicine Buddies (medicine dispensers shaped like a rocket ship and a butterfly) and two Booger Buddies (a light-up nasal aspirator elephant called Luna and a light-up nasal bulb narwhal named Noa, which was named one of The Best Inventions of 2025 by TIME). The products are available at Target and Babylist stores, and on Amazon through leading medical distributor McKesson Medical-Surgical.

Most companies design child healthcare devices with parents’ or doctors’ tastes in mind; none, however, focus on what will make kids smile, too.

TIME Best Inventions of 2025

in the Parenting Category, Playhouse MD's Light-Up Nasal Bulb

1 ½ years

from initial brainstorms to in-market launch of 4 kid-centric medical products

Verathon offers one of the broadest portfolios of video laryngoscopes on the market. While these life-saving devices increase the likelihood of successful first-time intubations, reducing trauma and speeding recovery times, they are more costly and can be more challenging to learn how to use than traditional mechanical ones, which rely on directly looking at the vocal cords. Wanting to make it easier for cost-conscious hospital administrators to transition from traditional laryngoscopes to Verathon’s video systems, the medical device manufacturer asked IDEO to technically and visually unite its next-gen reusable baton and stats, ensuring they worked together seamlessly, flexibly, and intuitively during the most routine and demanding healthcare moments.

‍The IDEO and Verathon teams used a prototype-led approach to safely gain feedback from anesthesiologists and emergency physicians. Research participants were given various intubation models to simulate surgical use cases and understand ergonomic trade-offs between different design directions. Insights from the field led to design principles such as prioritizing device balance over weight reduction, providing flexibility to accommodate a variety of gripping positions, making connection points between various parts clear, intuitive, and secure, and ensuring the device was easy to clean.

Inspired by physician feedback, the team designed an innovative video baton that felt both familiar and new, supported the ergonomics of diverse holding styles, and fit Verathon’s wide range of stat sizes (one of its competitive advantages). To prevent accidental disconnections, users receive clear tactile, visual, and sound feedback when the monitor and cover are correctly attached to the baton. IDEO chose color, materials, and finishes to help clinicians quickly distinguish between parts of the system and easily identify reusable components from single-use ones.

Verathon’s new GlideScope ClearFit stats-based video laryngoscope hit the market in August 2025. Combining one reusable baton with an industry-leading selection of six single-use stats, it’s compatible with both the GlideScope® Go™ 2 handheld and GlideScope® Core™ cart-based systems. Magnetic QuickConnect™ technology allows doctors to seamlessly switch between platforms, giving hospitals a standardized airway management solution that simplifies workflows, streamlines inventory, enhances medical education and training, and promotes cost-effective care.

Verathon invented video laryngoscopy technology. Twenty years later, the COVID-19 pandemic spurred more competitors to enter the market.

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Cutting-edge technologies like video laryngoscopes are critical—and costly—devices. Hospital systems want to invest in the most comprehensive, flexible, adaptable, and affordable tools for quality patient care

6 shapes and sizes

of single-use covers fit one video baton, enabling health workers to quickly access different throat anatomies

Since its founding in 1992 by the Gokongwei family, which also operates one of the nation’s largest conglomerates, JG Summit Holdings, GBF has supported Philippine education through student scholarships, teacher training funds, and donations to top universities. Over its 30-plus-year history, GBF has reached more than 200,000 teachers and impacted over 1.5 million learners nationwide. When the foundation approached IDEO in 2021, it wanted to move the needle on STEM education in the country’s struggling public schools.

The IDEO and GBF teams interviewed more than 100 subject-matter experts across various government agencies, leading technology companies, and top national universities and educational institutions to build a comprehensive understanding of the current state of STEM in the Philippines. To experience classroom struggles firsthand, the team also shadowed STEM educators and principals. The research revealed that time-strapped teachers needed help mastering overall STEM content, preparing for their classes, and selecting the most suitable activities for their students. They also didn’t always feel supported in their own career development journeys. But the burden of daily responsibilities didn’t leave them any time for outside learning opportunities, either. 

Recognizing that empowering teachers empowers students, the team created an innovative lesson prep tool, the GBF Class Builder, that's based on these insights. Using existing technology and course materials designed to align with school curricula, IDEO developed a working prototype of the Class Builder in under a week, with minimal investment. The team then systematically improved it over several “build-test-iterate” feedback cycles in real classrooms over the course of four months.

The final GBF Class Builder is a targeted program that helps STEM teachers deliver compelling lessons for their students, learn new teaching strategies, and build content mastery along the way. In addition to helping design the Class Builder, IDEO continues to advise GBF on key aspects of the program's piloting and rollout. This includes advising on how to implement the content development, think through the curriculum development process, support in designing feature iterations, define metrics for success, and decide which additional roles GBF will need to support the ambitious program going forward. 

The foundation launched the GBF Class Builder at its 30th-anniversary celebration in 2023. Feedback from pilot 3,500 public schools and 15,000 teachers has been overwhelmingly positive. “Through the GBF Class Builder, we envision our teachers to be more confident and effective in class, more ready to face the challenges of the future, while enjoying every class—feeling proud to be a teacher, " says Grace Colet, Executive Director of GBF.
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The ambitious program continues to scale and is on track to be rolled out in public schools nationwide by 2029.

90% of 10-year-olds in the Philippines can’t read a simple text, according to a Southeast Asia Primary Learning Metrics report. Eighty-three percent of Filipino 10-year-olds lack minimum proficiency in math.

More than 15,000 teachers in 3,500 public schools

are now using the innovative GBF Class Builder program
‍

94%

of teachers report the GBF Class Builder has improved student engagement, while 89% say it saves them 1-2 hours in prep time per lesson

Fizik came to IDEO with the aim of entering the helmet market with a range of high-end performance options fit for road racing, mountain biking, time trials/triathlons, and everyday cycling. Fizik planned to use its world-class production and engineering capabilities (the company is part of the Selle Royal Group, which has over 1,400 employees and manages five other cycling brands, including Crankbrothers and Brooks England). IDEO’s task was to develop an on-brand design language that would unify its new helmet line and integrate seamlessly with the sleek, modern aesthetics of the company’s other products.

The design sprint began with in-context interviews with avid and professional cyclists to learn what they wanted—and didn’t—in a helmet. Unlike other accessories, such as shoes or shorts, riders viewed helmets as a necessity rather than an exciting or aspirational purchase. They also wanted a helmet that complemented their overall style, rather than standing out, and had a flattering silhouette. Avoiding making a cyclist look like a “mushroom” was key.

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Different cycling styles involve varying riding postures, ranging from a dropped and horizontal position (time trial/triathlete) to an upright and vertical position (mountain biking). IDEO proposed a family of helmet designs that correspond accordingly. On the horizontal extreme, the time trial/triathlete helmet has the most fluid, organic, aerodynamic shape. A statement of refined simplicity and speed, it features minimal venting and a magnetic wrap-around visor to reduce drag that can be exchanged for the rider’s own cycling glasses.

On the vertical extreme, the mountain biking helmet features a more rugged, angular form with an adjustable brim, rectangular venting, and boxier, back-of-the-head protection. It also offers affordances for goggles, sunglass storage, a GoPro mount, and an optional integrated rear light.

In between, a compact helmet for road racing features evolutionary variations on these design extremes, with more venting as well as a front glasses holder and integrated rear light. All four helmets strike a balance between lightness, comfort, safety, and performance, embodying Fizik’s signature modern style.

In April 2025, Fizik launched all four helmet models simultaneously: Kassis (mountain biking), Kunee (trial/triathlon), Kudo, and the more aerodynamic Kudo Aero for road and gravel. The helmets were greeted with critical acclaim from the cycling world, while Virginia Tech’s independent helmet-safety research lab gave them five-star performance ratings for their strength and durability.‍

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Legendary cycling company Fizik was known for its high-performance bike seats and shoes, but wanted to be a fierce entrant in a challenging product category—helmets.

4

new helmet designs

1

new product category

Every CEO we talk to is focused on the same thing: using AI to become more efficient.

We call this the “AI Dividend.” The surplus of human bandwidth, creative energy, and organizational capacity that automation liberates. And right now, most leaders don’t have a strategy to reinvest it.

The organizations that win in the coming decade will not be the ones that automated fastest. They will be the ones that invested the resulting AI Dividend most wisely, redirecting it toward the unmeasured frontier where human creativity, judgment, and sensibility still reign. The AI Dividend is not a bonus. It is the seed capital for a fundamentally different kind of organization.

The efficiency trap

The instinct to automate is understandable. AI can now handle tasks that once consumed enormous human effort: synthesizing reports, managing logistics, coordinating schedules, and writing first drafts of code. Early adopters are seeing real gains. At Anthropic, they estimate that 90 percent of the code written to build Claude Code will soon be written by Claude Code itself. These numbers are not anomalies. They are signals.

But here’s the trap: If every competitor achieves the same efficiencies—and they will—then efficiency alone produces no lasting advantage. When the marginal cost of execution approaches zero, what differentiates one offering from another? Not speed. Not cost. The real advantage comes from pairing the efficiency gains with investment in an innovation capacity. 

The race to the bottom, where every product and service converges on the same AI-optimized median, isn’t a hypothetical. It’s already underway. Browse any social media feed, and you can see the first wave of it: AI-generated images, text, and video that looked novel six months ago, now blur into indistinguishable sameness. The term for this is "AI slop," and it will quickly become a problem for even the most well-intentioned companies. 

Leaders who focus only on efficiency will find themselves competing on price in a market where price advantages evaporate almost overnight. The smarter move is to think of efficiency not as the destination, but as the mechanism that creates room for something far more valuable.

The architecture problem

To understand why this moment matters, it helps to look at an earlier technological revolution.

When factories first electrified in the late 19th century, most owners simply swapped out the steam engine for an electric motor. They kept the same layout, the same belt-and-shaft system that transmitted power from a single central source to every machine on the floor. The factory was still designed around the constraint of steam. Everything had to be arranged near the power source, and the entire line ran at the same speed.

It took nearly 30 years for manufacturers to realize that the electric motor had completely changed the game. With small individual motors, you could put power at the point of use. You could rearrange the factory around the flow of production, not the flow of energy. The resistance to change was immense. Plant managers had spent careers optimizing the belt-and-shaft system. But once the unit-drive factory emerged, productivity gains dwarfed anything the old architecture could deliver.

Today’s knowledge-work organizations operate as modern belt-and-shaft factories architected not around value creation, but around the movement of information through large-scale enterprises. The layers of middle management, the endless meetings, the reporting structures, the approval chains—these are coordination mechanisms that evolved to solve a very specific problem. When the primary constraint on execution was how quickly and accurately information could flow between people, bureaucracy was the best available technology. Alfred P. Sloan understood the power of autonomy when he redesigned General Motors in the 1920s. He introduced what he called “coordinated autonomy,” giving division leaders more freedom to make market decisions while centralizing finance and operations. It was brilliant for its era. And every modern organization descends from it, but they have grown and been forced to add ever more layers of hierarchy to deal with the need for coordination. 

This is where the AI Dividend becomes transformative. Sloan’s vision can now be fully realized, and the resources that were once consumed by coordination and the organizational overhead of being big can now be redirected. The dividend is not just a few hours freed up on individual calendars. It is a structural surplus: the entire cost of managing complexity in ways that no longer require human intermediation.

Where the dividend should go

So, where should leaders invest this AI Dividend?

The answer is not “more of the same, faster.” The AI Dividend should be invested in a new, more nimble and dynamic organization at the creative frontier: exploring the unmeasured territory where AI models cannot yet operate and where human sensibility, intuition, and judgment create genuine differentiation. We see two opportunities at the creative frontier: to make existing ideas better and to create fundamentally new ones.

This is not abstract or aspirational. There are historical precedents, and they are remarkably apt.

When the Industrial Revolution flooded markets with cheap, uniform goods, the Arts and Crafts movement emerged in response. William Morris, working in 1870s England, looked at the mass-produced kitsch pouring out of industrial production and saw an opportunity. He built a practice around the premise that quality, taste, and creative judgment could produce things that machines could not replicate and that people would pay a premium for. From Morris came the entire modern design movement and the idea that, by making ideas better, quality and design can create differentiated value in response to technological commoditization. History is rhyming. AI is producing its own version of industrial slop, and the commercial response will follow a similar pattern. The organizations that invest in human creativity, taste, and judgment as a counter to algorithmic sameness will be fit for the new rules of competition. 

And there is a deeper layer to what makes this frontier valuable. Less frequently, but more dramatically, there are times when operating at the creative frontier can lead to fundamentally new discoveries. Consider Jackson Pollock. His drip paintings, created in the late 1940s, expressed fractal patterns with uncanny mathematical precision, decades before Benoît Mandelbrot described fractals as a formal mathematical concept. In the 1990s, a physicist proved that Pollock’s canvases contain precise fractal structures at multiple scales of magnification. Pollock was not doing math. He was operating in the territory between tacit knowledge and the unknown, sensing patterns that had not yet been codified and expressing them through a medium that had no algorithmic equivalent.

This is what the creative frontier looks like. It is the zone where human intuition grasps something real before systematic knowledge catches up. It is where new categories, new markets, and new possibilities are born. And it is precisely the territory that AI, by definition, cannot explore alone. 

A new architecture for productivity

Investing in the creative frontier is not simply a matter of telling people to be more creative. It requires building the organizational architecture that enables creativity and learning to be productive at scale. This is what we call the “Adaptive Organization.” 

An Adaptive Organization integrates both efficiency and innovation, optimizing around change rather than stability or consistency. It operates through small, highly autonomous teams that are maximally interconnected without hierarchical constraints, enabling fast parallel execution. Unlike traditional organizations built for predictable environments, it treats adaptability itself as the core competency—measured not by efficiency alone, but by the speed and capacity to evolve.

The blueprint for the Adaptive Organization is still being defined, but three early principles stand out:

1. Build smaller, more autonomous teams

The most productive organizations we observe today are not large hierarchies pursuing one strategy at a time. They are networks of small groups that operate with high autonomy and minimal coordination overhead. AI handles the connective tissue that used to require middle management: information sharing, resource allocation, and progress tracking. What remains is a team small enough to trust one another, move quickly, and take creative risks. The evidence is already strong. ElevenLabs organizes its 400 employees into 20 “micro teams” of five to ten people. Amazon’s “two-pizza teams” follow the same logic. Rather than hiring more managers, Moderna has deployed thousands of custom AI agents to automate coordination and has trained all 2,400 employees to be data-driven decision-makers.

2. Increase speed by operating in parallel

Legacy organizations often pursue one big bet at a time because their coordination costs make parallel activity prohibitively expensive. When AI absorbs those costs, you can run thousands of experiments simultaneously and execute the most promising strategies far more efficiently. In biological terms, it is the difference between a species that produces one offspring and bets everything on its survival and one that sends a thousand seeds into the wind. The latter adapts faster because it learns faster by doing. Every experiment that fails is data. Every experiment that succeeds is a new capability that accelerates progress.

3. Develop the creative capacity to translate the unknown into a competitive difference

This means investing in people and practices that operate at the frontier: people with curiosity, creative confidence, a bias to action, and a willingness to challenge dogma. Their job is not to optimize the known, but to sense what is coming next. It means building an organization that values judgment and taste, not as luxuries, but as the core capabilities that distinguish a premium offering from a commodity.

The compounding effect

Here is what makes the AI Dividend argument urgent rather than merely interesting: the dividend compounds.

When you invest freed-up capacity in the Adaptive Organization, you generate new insights and new possibilities. Those insights, fed back through AI-enabled systems, create new efficiencies and new capabilities, which free up more capacity, which you can invest in more exploration. It’s a flywheel. The organizations that invest the dividend first will not just have a head start. They will have a compounding advantage that accelerates over time.

Think of it this way. A company that uses AI only for efficiency is like someone who loses weight but never exercises. They are thinner, but not fitter. The company that invests the dividend in creative capacity is like someone who loses weight, feels more energy, starts exercising, builds strength, and finds they can do things they never could before. The fitness compounds. One change enables the next.

This also means that waiting is costly. The gap between early investors and late adopters will not be linear. It will be exponential. First movers who invest the dividend wisely get access to AI-accelerated creative tools, which let them invest even more productively, creating more advantage. The window for catching up narrows with each cycle.

The gardener’s mindset

None of this can be commanded into existence. You cannot mandate creativity. You cannot engineer emergence. And this is where leadership itself must change.

The leader of a belt-and-shaft factory was, appropriately, an engineer. The leader of a dividend-investing, Adaptive Organization is more like a gardener. The gardener does not make the plants grow. The gardener creates the conditions in which growth happens: the right soil, the right light, the right spacing, the right pruning. The gardener sets boundaries and provides resources. And then the gardener gets out of the way.

This means designing organizational structures that enable rather than direct. It means tolerating ambiguity and partial answers. It means understanding that the creative frontier, by definition, cannot be mapped in advance. You are not building a machine. You are cultivating an ecosystem.

The instinct of most leaders under pressure is to tighten control, to demand predictability, to optimize harder. That instinct is perfectly suited to the belt-and-shaft factory. It is lethal in an environment where the competitive advantage comes from adaptation, from sensing and responding to changes faster than the world throws them at you.

The average lifespan of a Fortune 500 company has been declining for decades. Not because these companies got worse at what they do, but because the environment around them changed faster than they could adapt to. AI has the potential to reverse that trend, but only if leaders use it to increase their adaptive capacity, not just their operational efficiency.

The dividend is real. It is already accumulating.

The door to the new economy is open. Are you willing to step through it?

You can find more of Tim and Joe's explorations of the future at theoasis.press.

“The smarter move is to think of efficiency not as the destination, but as the mechanism that creates room for something far more valuable."

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If you ask consumers whether they trust their bank or health insurer, the answer is often lukewarm at best. Traditional financial institutions are seen as opaque, distant, and—in the worst cases—extractive. In a recent survey, barely a third of people thought their bank was being honest and transparent about costs and fees. But this narrative misses a subtler, more hopeful trend: The very technologies customers once feared as “creepy” are now opening a path toward rebuilding trust—not by hiding behind compliance and branding, but by providing real, personal value that people experience every day.

At the heart of that opportunity is AI. But not the generic, algorithm-as-black-box that so often gets headlines in the press. I’m talking about AI designed to help people, surfaced in ways that feel insightful rather than intrusive, and that elevates the relationship between individual and institution. It has the potential to rewrite our skepticism of digital products. 

The myth of the “creepy algorithm”

We’ve long assumed that consumers recoil at AI and the use of personal data. But emerging evidence and our own work at IDEO suggest a more nuanced truth: People don’t mind institutions using their data if the outcome is genuinely helpful.

What consumers resist is not personalization per se, but value extraction masquerading as personalization. They reject opaque upsells, hidden fees, and pricing strategies that leverage data solely to maximize profit. Anyone who has filed their taxes with the most popular online filing software can attest to the repeated (and difficult to decline) requests to use their tax data and share it with third parties, or to pay for add-on features of dubious value. But consumers welcome insights that feel like guidance—a personalized budget forecast that suggests paying down a mortgage faster, or a care recommendation that anticipates a chronic condition based on patterns in medical claims.

Financial institutions need to know their customers to help them meet their goals, not just the company’s bottom line.

When AI becomes a bridge, not a barrier

This distinction reframes the role of AI from a technical feature to a trust-building mechanism across two dimensions:

Predictive empathy: AI can surface insights that anticipate people’s needs before they articulate them. Imagine a health insurer that detects early patterns of risk in claims data and nudges members toward preventive care—with clear explanations and options—rather than waiting for a crisis.

Clarifying complexity: Financial and health decisions are inherently complicated. Consumers often feel alone with high-stakes choices—choosing a plan, managing costs, planning a life event. AI can simplify decision pathways, outline “what good looks like,” and benchmark personal choices against broader patterns of positive outcomes.

This is not theoretical. Customers are more open to institution-driven AI guidance than third-party general-purpose AI tools, precisely because of contextual relevance. While 79 percent of US consumers are uncomfortable with AI providing medical advice, a majority are open to AI supporting tasks like after-visit summaries and follow-ups.

In recent work we completed for a network of financial advisors, for example, we found that a customer’s trust in their financial advisor alone was not enough. They needed to believe that the plan presented to them met their needs and was likely to succeed, given the current market and their own financial situation. Unfortunately, too many financial institutions are seeing an opportunity to replace people with machines. When, in fact, there’s now an entirely new opportunity space of using generative AI to show, rather than tell, customers why they should have confidence in the path forward. People build trust. Digital tools can build confidence through what-if scenario planning, natural-language explanations, and the ability to have back-and-forth conversations whenever people want, without fear of asking “stupid” questions.

The unleveraged assets: contextual data and human intent

Banks service millions of accounts across life stages. Health insurers care for diverse populations across care journeys. These institutions are regulated to protect consumers, and in theory, succeed when their customers succeed. Yet traditionally, the relationship has been transactional: accounts, premiums, claims, and products.

AI changes that calculus. With careful design, institutions can translate the data they already hold into guidance that feels humane and useful. What might that look like?

• A bank dashboard that explains how spending patterns today impact long-term financial goals tomorrow in plain language, with actionable suggestions.

• A health insurance experience that models likely care pathways based on clinical evidence, population outcomes, and expected out-of-pocket costs, helping members choose care confidently.

• Alerts that not only flag risk—say, missed medication adherence—but also provide contextual support options, such as care coaches or financial assistance resources.

We don’t have to imagine far into the future to see what this looks like—we can actually see it in the not-too-distant past. When IDEO worked with PNC to develop their online banking platform, Virtual Wallet, one of the most compelling features for customers was a calendar that visualized “Danger Days”—days when their balances would be low, and they were most likely to overdraft. During a time when banks were making big profits by optimizing their technology to increase overdraft fees, PNC went in the opposite direction, helping customers avoid fees they despised and improve their financial lives. The result was an award-winning product that led to millions of new accounts.

This reframes data use from an internal optimization tool to a shared resource that benefits the member first. As banks bring generative and agentic AI to the masses, that could start to look like suggesting specific actions customers can take to reduce spending when cash flow is tight, or automatically splitting bills that fluctuate in cost, such as groceries and utilities.

Why incumbents have a strategic advantage

New fintech and health tech startups often don’t have to contend with years of technology debt and legacy systems, but they do suffer from a cold-start problem: They lack deep context. They may ask users to upload data, recreate history, or deduce preferences from limited interactions. Meanwhile, incumbent banks and insurers already have longitudinal data spanning life events, regulatory frameworks ensuring baseline protections, and direct experience managing risk at scale.

These are more than just advantages—they are assets waiting to be mobilized to build trust.

A new contract between people and financial institutions

Rebuilding trust in financial services and health insurance isn’t just a matter of better algorithms. It’s about redesigning relationships and using AI to make institutions advocates for individual success.

It’s easy to imagine continuing with the status quo: adding a chat interface to a mobile banking app that relies on existing functionality, for example, or offloading the most commonly-requested customer support tasks to an agent to reduce call center volume. 

That’s not to say that there isn’t merit in some of those approaches. But, in a world where AI can feel impersonal and inscrutable, there is an opportunity for these institutions to shine by making AI personal. That’s not only good design. It’s good business, and it’s good for society. The question isn’t whether AI can help. It’s whether we will use AI to help people—boldly, compassionately, and with trust at the center.

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Working to build trust with your customers? We’d love to help. Get in touch.

“People don’t mind institutions using their data if the outcome is genuinely helpful."

For decades, the typical relationship between humans and hardware was a straightforward one of command and control. We pushed buttons; machines performed tasks. But as AI migrates from our screens into our physical environments, the nature of that relationship is shifting: Computation is becoming a nondeterministic presence.

So far, the industry seems to be caught between what users actually want and the sci-fi fantasies currently captivating the tech community. Rather than marrying what’s technologically feasible with what people need, companies are bolting AI onto everything, shipping tech demos as finished products. The result is half-baked experiences traded for focus, and speed traded for agency and privacy. A better path means establishing a new standard in which trusted, useful intelligence serves the physical world.

For us, it means designing under these seven principles for good AI hardware, inspired by the timeless rigor of Dieter Rams, and setting the conditions to put them into practice. 

1. Good AI hardware is mutualistic

AI is not a deterministic layer of software trapped in plastic. Designed well, it is a partner in human flourishing. We build hardware rooted in mutualism: the device learns from the perpetually shifting nuances of human intention, and the human grows through what the device makes possible. More than optimization, it is a shared evolution between person and machine.

What might that look like?

The Household Reflection Mirror

A bathroom mirror is designed without predetermined health goals. It comes equipped with sensors, models, and processing power, but without a built-in definition of what “better” means for you. That definition emerges through use.

In the first few weeks, the mirror observes your behaviors without interpreting them. Over time, patterns surface that you implicitly confirm or contradict through your actions. You linger on certain readouts. You ignore others. Slowly, the device constructs a model of what matters to you, not what a wellness framework says you should care about.

After three months, the mirror notices your sleep quality degrades during a particular recurring time of the week. On Monday morning it surfaces a single line beneath your reflection: “Sleep is typically shorter on Mondays.” The next Sunday, you go to bed an hour earlier. The mirror notices. Over time it learns what kind of attention you actually respond to, because you taught it through the way you live and react. The device you have at the end of the year is not the one you started with—and neither are you.

Without your input, it is a capable system with no inherent sense of purpose. Without it, you miss a form of self-knowledge you didn’t know you needed. Closing that gap together is the essence of mutualism.

2. Good AI hardware is off by default

Truly human-centered hardware requires intentional consent to engage. Privacy isn’t an afterthought; it is a foundational design requirement. Until there is consent and utility, we reject the always-on model of technology. We design to respect the privacy of our homes and lives, so that technology enters our cognitive space or observes our physical space only when we consent. Good AI hardware is something you want around, that makes you feel safe.

What might that look like? 

The Consent Door

A front door knob has a small illuminated ring at its base. When you arrive home, the ring pulses once, dimly and unhurried. It is asking for your attention.

Twist the knob the way you normally would to unlock the door, and the ring goes dark. The house stays quiet. No sensors wake. No systems are activated. You are home, yet the home does not know it.

Twist the knob the opposite way before you enter, and the ring glows brightly. The AI features of the home come online. The thermostat starts learning. The mirror observes. The kitchen listens. You’ve made a conscious choice with your body before you crossed the threshold.

Guests see the same dim, illuminated ring when they arrive. They face the same choice. The house never assumes.

3. Good AI hardware is aesthetic

A piece of hardware should improve the aesthetic or emotional vibe of a space or interaction. With all the focus on the AI, it’s easy to lose sight of how intentional and sensorial the hardware has to be to provide value. We lean into creativity by using ergonomic forms, tactile textures, and haptics to create objects that feel like art or furniture while having capabilities suited to their use. The best AI hardware is crafted with intention for its role in our environments. 

What might that look like? 

Climate-Aware Window Glass

A sheet of AI-embedded  glass replaces a traditional living room window. The glass learns sunlight patterns, outside temperatures, and occupants’ daily rhythms. When the afternoon sun becomes harsh, the glass subtly softens and diffuses the light, giving the room a warm, painterly glow. On cold winter mornings, it lets in full sunlight to warm the space. There are no visible controls and no notifications. The window functions as an intelligent material, shaping light and heat in ways that feel natural, healthy, and beautiful.

4. Good AI hardware makes the invisible honest

AI hardware should communicate its state, data usage, and limitations through intuitive physical cues. Sensors now extend far beyond cameras and microphones into biosignals, radar, emotion recognition, and neural intent. Technology can understand us in unprecedented ways, which creates unprecedented room for distrust. It’s crucial that we replace vague terms and conditions and labyrinth privacy settings with transparent, real-time feedback, and make the invisible visible. Honesty is the only foundation for a lasting relationship with intelligent technology.

What might that look like? 

Visible Cognition Display

A small home security camera sits near the front door. Inside the door is a narrow strip that displays what the AI believes it sees in plain language. When someone approaches at night, it writes: “Face scan attempted. Low light. Result unreliable. Door stayed locked.”

The system reaches the edge of what it can confidently do and stops there, telling you exactly why—and that restraint is the point. A device that surfaces its own limitations in real time is making a fundamentally different promise than one that acts with false confidence and hides its reasoning. That promise, repeated across thousands of small moments, is how trust is genuinely built.

5. Good AI hardware is minimally intrusive

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The ultimate goal is to improve our lives in the physical world, not to keep us tethered to a digital one. We need to design for the disappearing act by ruthlessly stripping away unnecessary screens and notifications, and removing friction between humans and their environments. We need to design to follow user intent, using the superpowers of AI to understand user needs and respond in simple, clear ways. Whenever possible, we embed AI into existing rhythms and daily flows, rather than requiring people to pick up new habits. 

What might that look like?

The Context-Aware Notification Pebble

A small, smooth stone is connected to your devices. When something truly important comes in—a call from family, a hard-to-get dinner reservation, a flight deal on a trip you’ve been planning—the pebble gently glows. There are no sounds. No vibrations. No screens. Just light.

If you pick it up, the message is faintly projected onto the desk. For matters that require a decision, the system has already done the legwork. It held the reservation. It queued the flight deal. The pebble presents you with a choice, not a task. A single tap confirms your decision. The pebble stops glowing, and life continues as normal.

If you choose to ignore it, the pebble turns off on its own. The system respects your attention instead of competing for it.

6. Good AI hardware is honest about its capabilities 

We design hardware that delivers on what technology can actually do, while meeting real user needs. Good design speculates and continues to evolve as technology improves, but it does not hinge on speculative promises it can’t meet (and which inevitably let users down). We focus on the high-fidelity reality of what a device can do now, making the hardware a reliable anchor as technology improves rather than a vessel of far-reaching hype.

What might that look like? 

The Tutor Tablet for Children

A child asks the intelligent tablet a complicated science question. The device pauses and displays the message: “I am not certain about this answer…  Would you like me to look it up with you?” When the model is unsure, it offers sources or invites the child to explore the topic together. Sometimes it even says: “I might be wrong. Let’s test it…”

In this way, the child learns two important lessons: the scientific method and the understanding that intelligence, whether human or artificial, involves humility.

7. Good AI hardware respects the Earth

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We reject creating unnecessary devices that contribute to the global e-waste crisis by staying attuned to real user needs. When we build, we design for longevity through repairability, modularity, and circularity. From the selection of materials to the ease of recycling at the end of a product’s life, good AI hardware takes responsibility for its physical footprint. We build tools that are meant to last, not to be discarded when the next software update arrives.

What might that look like?

The Modular Home Intelligence Hub

A small wood and aluminum hub sits on a shelf, running local AI models for the home. Each component slides out like a drawer: Compute, Storage, Radio, Battery, and Sensor Array. While these modules are functionally co-dependent, each one is discrete. For instance, whenever new AI chips are released, the owner can simply replace only the Compute drawer.

The device also maintains a lifetime material dashboard:

Operational life: 8 years
Upgraded components: 2
Material saved vs. replaced: 4.3 kg
Energy used this month: 2.1 kWh

The hub processes most workloads locally to minimize unnecessary cloud energy consumption. Instead of being replaced every few years, it ages alongside the household.

Putting the principles into practice 

It’s one thing to create a list of principles. It’s another to make them part of our work. To uncover real human needs, we do research with people in their homes, learning where they want machines to help and where they want to stay in control. Leveraging emerging tools like 3D printing, AI-accelerated prototyping, and generative design, we move from insight to artifact quickly. Rather than just rendering a sensor, we build it. Rather than speculating about how friction feels, we test a physical interface in someone’s home and let their reaction tell us what to do next. We hold creativity accountable to what’s technically viable and commercially scalable, so the visions we make are ones businesses can actually act on.

We are moving past the era of experimental AI and into the era of specialized, high-performance hardware that permeates our everyday lives. The only way to succeed in the market is to earn a permanent place in people’s  lives through trust and utility. By anchoring what we build in these principles, we move beyond the hype and into producing hardware that people actually want around. That is the standard we are building toward.

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(Looking for a partner to build AI products with? Get in touch.)

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"A better path means establishing a new standard in which trusted, useful intelligence serves the physical world."

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Founded in 2021, Callimacus is a small collective of mathematicians, engineers, artists, and philosophers exploring how new technologies can rewrite the web experience. Their guiding principle, “human artificial intelligence,” emphasizes designing systems that complement human values, highlight emotional nuance, and enhance human presence. 

So far, so San-Francisco-based-start-up, you might say. But, in fact, Callimacus swaps the hills of the Bay for the slopes of Solomeo, a medieval village in Perugia, Italy. Instead of a tech billionaire, its patron is a titan of luxury fashion: Brunello Cucinelli. 

‍As unexpected as that might sound, Cucinelli himself has been a passionate advocate of what he’s called the “humanistic enterprise,” born of his deep interest in philosophy. So while Callimacus might be pioneering a radical rethinking of e-commerce by challenging traditional established web and user experience norms, that’s firmly grounded in the brand’s human-centered DNA.

Callimacus's first product is an AI-native software development kit aptly called Callimacus, which powers Brunello Cucinelli’s new digital experience. Named after the ancient Greek poet and curator of the Great Library of Alexandria, Callimacus uses custom AI agents to dynamically generate the user experience based on user intent. For instance, the site’s agents can determine that a visitor is looking to learn about the story behind the brand (and serve them content, accordingly) or that they’re here to see the latest collection. It’s a custom experience very different from a traditional page-and-container website structure. 

I spoke with Francesco Bottigliero, Brunello Cucinelli’s Chief of Humanistic Technology and the CEO of Callimacus, to understand why the brand created the start-up, the importance of keeping humans at the heart of innovation, and reimagining UX and engineering for the AI era.

Ed White (EW): What was the inspiration behind Callimacus?

Francesco Bottigliero (FB): The French philosopher Voltaire once said, “Doubt is not an agreeable condition, but certainty is an absurd one.” This led us to think, “Our friends from Silicon Valley believe AI will be a game-changer, so let’s take the time to understand it.”

At Brunello Cucinelli, we’re focused on learning and staying current in our industry, rather than simply following trends. We choose technologies that align with our values, ensuring we don’t compromise the craftsmanship and brand positioning we’ve built over more than 40 years. In our boutiques, we aim to make technology invisible so there’s no barrier between sales associates and customers.  

We’re investing heavily in our brick-and-mortar distribution to stand out and nurture our brand identity, while also enhancing and developing our online identity. 

‍EW: Callimacus emphasizes “human artificial intelligence.” Can you explain what you mean by that?

FB: We view technology as a companion of humanity. We want human beings at the helm, steering the boat. However, if there are tools that can help people steering the boat make better decisions, we are ready to embrace them. 

EW: You recently launched an AI-driven web experience for Brunello Cucinelli using the Callimacus software development kit, which enables brands to create AI-native web experiences. Where did the idea for that come from? 

FB: When we looked at traditional e-commerce, we analyzed the navigation of six sites from six brands. We masked the brand names and placed the sites side by side. They all looked the same.

We decided to experiment with new ways to communicate our story more effectively. Today, when people talk about applying AI to e-commerce websites, they typically mean adding a chatbot. However, the underlying infrastructure remains the same, relying on page formats—whether product, listing, or article pages.

EW: Why are traditional web pages such a problem?

FB: Websites continue to be designed and developed using a typographic approach that has been in use for more than 30 years, dating back to the beginnings of the internet at CERN in Geneva. Users shared research papers online, and they were read as if they were physical books. The interaction mimicked print media by incorporating pages and menus people were already familiar with. Likewise, when I visit Corriere della Sera, Italy’s leading online newspaper, all its content is locked within a traditional page-and-container structure, making it difficult for me to search for something using natural language. The same issues apply to e-commerce sites.

Callimacus offers a completely different approach to website design. It functions like an orchestra, integrating multiple agents running on different large language models.

Callimacus is based on three fundamental ideas: First, we eliminated content containers, allowing content to flow and be recombined. We call it “page-less design.” Second, instead of starting with user interfaces or framework templates filled with placeholder text, we began by spotting signals of the customer’s intent and the experience and journey we wanted to offer. Third, enabled by the first two ideas, we created a personalization engine that delivers a tailored experience for each user. 

EW: How does that work?

FB: One of our agents, Thamyr, named after the ancient Greek painter, continuously analyzes the user’s intent—for example, getting inspired or product search—and updates the website in real time. Each interaction the user has with the website refines this intent. Thamyr might say, “It seems like this user is more interested in product search or is seeking inspiration.” She then goes to our agent, Socrates, and says, “Hey, please provide me with products the customer might be interested in.” However, it turns out the user is not really into the product but rather wants to explore the brand. So Thamyr decides to feature fewer products, but more editorial content. She then references the design system, selects the appropriate components, and builds the interface accordingly.

EW: How does all this “intelligence” impact the back end?

FB: One of the advantages of the platform is that editing websites can become a conversational experience. For example, you can say to Callimacus, “Hey, we just received the new capsule tennis collection.” Callimacus replies, “Okay, do you have pictures?” You say, “Yes, they’re here in this Dropbox folder.” Callimacus then asks, “Okay, do you have the prices?” “Yes,” you reply, “this is the spreadsheet with prices.” Once the files are uploaded, Callimacus says, “I’m ready to publish.” Then you confirm, tell it to publish, and it’s done. Since Callimacus generates the interface, it decides which content is displayed to the user. 

You can also interact with Callimacus by asking it how the collection campaign is performing in Japan, for example. Callimacus might reply, “It seems we are performing worse than last week.” In which case, you could ask it for suggestions on how to improve. It might respond, “Perhaps we could increase the frequency of displaying these collection banners in specific customer journeys?” If you agree, you simply tell it to execute that plan.

EW: What’s been the response to the new Brunello Cucinelli experience? 

FB: The initial reactions are very positive, and the metrics reflect this. We’re seeing increases in engagement rate, time spent on the website, and items added to the cart.

‍EW: Given your successes so far, what are you hoping to see next?

FB: In five years, I envision an experience that feels more like shopping in a physical boutique. I want to be able to interact with the system using voice commands and have it understand what I’m looking for, rather than forcing me to search endlessly for the right item. It should respect my privacy, personalizing the experience without relying on third-party data. The process should be quick and intelligent, recognizing that I’m either on my phone and in a hurry, needing to buy a last-minute birthday gift for my wife, or that I’m relaxing in an airport lounge and would like to learn more about the new collection, or about the hamlet of Solomeo, or do a deep dive into craftsmanship. 

We finally have the chance to go beyond decision trees and pre-defined rule-based logic, to make digital applications more intelligent and genuinely respectful of people. It’s a great chance to rethink, from the foundation up, how digital technology works and the business models we have adopted to run it.

“Design in the Age of AI” is a series of conversations with designers and makers from across industries and disciplines, building the future with AI, today.

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“We want human beings at the helm, steering the boat. However, if there are tools that can help people steering the boat make better decisions, we are ready to embrace them."

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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.