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2012. I walk out of a gastroenterologists office with a brochure titled Your Life With Ulcerative Colitis. What the brochure doesnt say: A month later, I will wake up on the day of a critical midyear design presentation feeling too nauseous to leave my apartment, and will have to spend several weeks at my parents house, where I will miss several more midterms. A year later, Ill stand at a boarding gate and feel too sick to take a five-hour flight and meet with potential graduate school advisers. Ill soon learn that, for me, these wont be one-offs. Instead, Ill live a life of constant flux, impossible to plan for. Desperate for some control as I push through academia, I turn to tech products. But technology cant help me. Digital tools excel at routines, but falter at exceptions. I can schedule weeks of meetings in a few clicks, but when Im unwell, Im copy-pasting the same cancellation message a dozen times. My personal-finance app keeps me on track, but only until an urgent-care bill throws things off. When my fitness tracker chastises me for not closing my rings during a particularly brutal flare-up, I shove it into my junk drawer. Technology is failing me when I need it the most. Happy paths 2016. I join Big Tech, working as a user researcher in early-stage and AI technology. Two things become immediately clear. First, my story is far from unique. Anecdotes from many hundreds of user interviews reflect lives riddled with chaos and disruption. Changeunplanned and plannedis the norm. Second, consumer products are largely designed for happy paths. A clear-cut problem is solved by a superhero technology, resulting in a favorable outcome that is tied off with a neat bow. For the sake of clarity, efficiency, and technical ease, the zigzag realities of lives are often sanitized into an idealized arc. We trot out these squeaky clean stories as hero use cases for a product ideafirst to convince ourselves, then our executives, and, finally, our users. Todays explosion of consumer-facing GenAI products are built with the same recipe. We get heartstring-tugging stories with just enough complexity to feel real, without any of the mess. A dad uses AI to prepare for a job interview while reminiscing on parenthood. A parent brings a childs imaginary creature to life in a custom picture book. Some brands try to incorporate more chaotic realities (a storm hits restaurant patio seating) only to portray absurd overdependence on AI (waiters leave their customers drenched because an AI agent doesnt reseat them indoors). If youre like me, these ads make you want to scream: Youre standing in the middle of the kitchen. How are your kids not interrupting your conversation with AI 27 times? But in contrast to the hero use case, taking kid snack breaks and asking AI to repeat itself over the noise of toddler screams are often cordoned off as edge cases in product development. The implication: These occurrences are rare. But they arent. Human journeys are not straight lines. They are dynamic, defined by change, interruptions, and curveballs. Some 60% of Americans reported experiencing an unexpected expense in the past year, though 42% dont have an emergency fund greater than $1,000. Households with two or more children have a viral infection in the household more than 50% of the time. And an estimated 28% of work time each year is lost to distractions. When technology isnt resilient to this reality, it breakssometimes catastrophically. Like when a Florida teen dies by suicide after his lengthy conversations with a Character.ai chatbot turn darkly romantic. When AI-powered cameras mounted on public buses mistakenly ticket thousands of legally parked vehicles in New York because they fail to recognize alternate side zones. Or when AI weather models fail to predict the worst storms because extreme weather data doesnt exist in the training data. These outcomes are extreme, but the pathways leading there are deeply ordinary, broken by nascent technology that isnt resilient to the gritty reality of human behavior. Sometimes, the catalyst stems from the tech itself, like security vulnerabilities. Other times, its agnostic of the technology, like mental health. But in all cases, the technology was not resilient to changes in context. AI’s broken promise Years ago, you could blame technology as the limiting factor. But AI should, ideally, thrive on this sort of complexityusing its superpowers of pattern recognition, synthesis, and triangulation of thousands of data points about users and their environment. GenAI has introduced a new frontier around deep reasoning and human interaction that should make the technology more tractable and transparent. AI is uniquely positioned to help people anticipate and recover from change, the kind that they may not have seen coming. Yet the Character.ai system didnt raise the alarm when a conversation overtly turned dangerous, much less recognize patterns that may suggest that it was headed that way. On issuing its 7,000th ticket in one day, the MTAs system didnt flag that this is an unusually large number of violations on a route. Its never easy to deal with the complex behavior of humans and societies. But when we keep designing to make already great lives 1% better, we are perpetuating a specific type of harmone that happens when the people designing the technology arent considering the real ways it might be used. As UX practitioners, we are uniquely positioned to start the conversation about how to change this. To move toward an AI UX rooted in resilience, well need to shepherd at least three main shifts in the way our products are designed. 1. Shift the user stories we tellwhich directly map to the problems we choose to solve. UX must choose to foreground the hard, complex story. We all have one: a multigenerational household with life-stage changes, moves across the country, divorce, job loss, a chronic illness. Right now, a key barrier to centering these stories is that they extend ideation cycles, which is uncomfortable in an increasingly launch-first-or-perish climate. As a result, cleaner stories, like the product narratives described earlier, win out. To break this cycle, UX can introduce complex user stories to product teams starting with ideation, through prototype and concept testingespecially ones that cut horizontally across product ecosystems. This requires creating a new canon: an accessible taxonomy of types of complexity, curveballs, and changes that we can easily pull from. Such a taxonomy might take the form of brainstorming prompts, user journey templates, or a card deck or visualization used in sprints. This cracking open will take time, but the more we tell these stories, the easier thy will roll off the tongue, and the more they can become normalized. 2. Shift how we leverage user data in AI-powered products. Today, user data collected by companieswhile wide-rangingisnt always curated or connected well. Most users, particularly younger generations, have resigned themselves to data collection and dont mind it, but also dont understand how the data is used or whether it benefits them. This is not an argument to collect more data. Rather, its a call to connect existing data for more meaningful, tangible user benefits, like helping navigate blind spots and complexity. Consider a simple example: Anns AI agent has access to a calendar app where she has blocked off time for a post-work run, a weather app that shows unexpected evening rain showers, and a maps app that she frequently uses to navigate to a yoga studio. This agent can now surface a timely suggestion: help Ann move meetings to shift the run to earlier in the day, or help her find a class at the yoga studio at that time. In reflecting how people really use their technology, this sort of cross-product dialogue and synthesis has the opportunity to leverage AI and user data to unlock resilience in the face of change. 3. Shift away from traditional definitions of seamlessness and magic moments toward ones that gracefully embrace failure, meaningful friction, and deep, explicit user feedback. AI advancements tend to tempt product teams to remove all friction and present users with auto-magical solutions to needs they werent even aware of, from hyper-personalized AI-driven ads to smart nudges on food and shopping apps. Common success metrics used today reflect the value we place on frictionless experiences: fewer clicks, greater session length, engagement with automation features, fewer user-submitted comments. This can cause a misleading overreliance on implicit behavioral signals that dont always reflect real intent. Take the example of an in-app pop-up: A user might spend a long time viewing it, even clicking on a linknot because they find it useful but because they cant find the exit. Even when users do provide explicit feedback, its often not in a form that can be interpreted meaningfully, leading to undesired outcomes. Think, for example, of how OpenAIs models grew sycophantic after a thumbs-up on a response was used as a signal to make the chatbot behave more in that direction. Instead, how might we offer users more ways to provide granular feedback that can shed light not only on the what but also the why? This can be meaningful friction that can empower users to have their unique human context be better understood while harnessing the beyond-human capabilities of AI. One could argue that this, in fact, is the more magical experience. Finally, the pursuit of seamless perfection risks underplaying the shortcomings of AI itselfmisunderstood accents, factual inaccuracies, biased imagery. These are a function of the technology, and are bound to happen. UX needs to treat these as predictable breaking points in the technology, build frameworks to classify them, and design intentionally with them as part of the user narrative. Of course, its far simpler to sketch these solutions than implement them, but if AI is to work well for real-world problems, we need to tackle real-world complexity head-on. UX is in a powerful position to shift these mindsets. As it has done for domains like accessibility and product inclusion, UX can redefine the problems and narratives that emerging technology is built for, and reshape the UX to accommodate product and user realities to support resilience. Are we brave enough to get into the messy weeds and do it?
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Mining isnt known for innovation. For more than a century, weve extracted copper using the same process: dig, crush, grind, leach, repeat. Meanwhile, demand has exploded, fueled by EVs, AI infrastructure, and the energy transition. That mismatch has created a bottleneck. Were using yesterdays tools to power tomorrows economy. The conductive highway Copper is the metal that moves energy. Literally, electrons dont travel from solar panels to batteriesor from your laptop charger to the cloudwithout it. Copper is the conductive highway that keeps the worlds electrons flowing. Its in every EV, every wind turbine, and every data center. Its also in short supply. Weve mined the easy stuff. Now were left with lower-quality ores, deeper deposits, and rising costsjust as demand hits historic highs. And when the global economy is built on electrons, copper is no longer just a commodity. Its a strategic resource, central to national security, electrification, and economic stability. Global copper demand is projected to reach 50 million metric tonnes annually by 2035double todays levels. According to BloombergNEF, the world needs over $2 trillion in mining investment by 2050 to meet electrification targets. Meanwhile, ore grades have declined more than 40% since 1990. Investors are watching this gap, and innovation must step in. Innovative microbes But something big is happening underground. And I mean that literallywhere the cool rocks are and things get interesting. As a scientist, I spent years working on astrobiology, cloud platforms, and energy systems. Ive seen how cross-disciplinary thinking can unlock entire industries. Today, I lead a team using engineered microbes to recover copper from ore that conventional mining leaves behind. It sounds unusual, and it is. But thats the point. Innovation in mining doesn’t come from fitting init comes from standing out. Mining is a deeply conservative industry, and for good reason. Even small changes carry massive financial and operational risks when your tools move millions of tons of earth. But thats also what makes this moment so powerful: When something new works, it really matters, especially when it can be plugged into existing infrastructure without requiring entirely new capital build-outs. Juice from a rock At Endolith, we recently completed testing with BHP, one of the worlds largest mining companies, through their Think & Act Differently (TAD) BioMetals innovation program. Our microbes were tested under simulated field conditions on a low-grade primary sulfide orea material so complex most operators consider it uneconomic to process. In one study, microbes shaped through adaptive laboratory evolution and guided by AI recovered up to 80% more copper from this material. Thats like squeezing juice from a rockand getting nearly twice as much. And this wasnt just a lab trick. These microbes work in real mining environments. They dont need clean rooms or perfect conditions. They need oxygen, acidity, and timeconditions already present in heap leach operations worldwide. We didnt reimagine the entire mine. We made the part most people had written off valuable again, making it cheaper, cleaner, and easier to operate. By using microbes that require no expensive reagents or intensive energy inputs, were cutting both capital expenditures and operating expenses, making recovery from low-grade ore economically viable again. Leapfrog technologies Heres why that matters. Ore grades are falling. Permitting timelines stretch for decades. Investors and regulators demand lower impact, higher performance, and real ESG outcomes. Mining companies know the status quo is unsustainable, but risk makes experimentation difficult. Most “sustainable mining” efforts rely on incremental gains: better water management, slightly lower emissions, and somewhat faster recovery. Important? Yes. Transformative? Not even close. We need leapfrog technologiesnew tools that unlock value, speed, and sustainability together. Biology is one of those tools, and right now, its underused. Biology belongs in the core toolkit of modern extraction. CRISPR for rocks Industrial biotechnology has already transformed medicine and agriculture, unlocking precision, efficiency, and resilience at scale. Its time for mining to catch up. Think of this as CRISPR for rocks. Instead of blasting ore with chemicals, we let microbes do the work. They break down rock, extract metals, and leave far less waste behind. With help from cloud-based systems, we can tune that process in real time, adjusting to changes in temperature, pH, or ore composition. Similar biological platforms could be applied to rare earths, lithium, and other minerals critical to the clean energy economy. The opportunity here is massivenot just for Endolith but for a new generation of industrial innovators focused on extraction rather than consumption. As governments prioritize mineral independence and ESG compliance, scalable bio-based solutions are becoming essential to securing the future of energy, technology, and defense. Scaling this kind of innovation takes more than strong results. It takes strong partnerships between startups and majors, scientists and operators, and regulators and entrepreneurs. We found that with BHP and the TAD team. They gave us a shot. We delivered. And now were working with others to bring this to production. But scaling also requires trust in the science, in the process, and in the promise of doing things differently. It means rethinking how we define innovation in mining and giving ourselves permission to imagine something beyond the current constraints. A systems problem People tend to talk about clean tech and hard tech as if theyre separate. EVs go in one box, mining goes in another. But thats a false split. There is no clean energy without minerals, no electrification without copper, and no scalable, sustainable supply without reimagining how we recover it. This is a systems problem, and it requires systems thinking. That reimagining wont come from status quo thinking. Itll come from radical collaborationand from being brave enough to try something different underground. Itll come from leaders willing to back bold science and turn pilot results into platform change. Heres the thing: I used to study how life evolved on Earth billions of years ago. The most extraordinary life forms Ive worked with? Theyre here on Earth today. Deep in the rocks, quietly solving problems we’ve struggled with for decades. So, if you want to power the future, start by listening to the ground and the weird, wonderful microbes doing the heavy lifting. In a world racing toward electrification, these tiny organisms just might be our biggest asset.
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E-Commerce
Three years after its launch, Perplexity is still struggling to break through. A major hardware deal could change that. On Sunday, Bloomberg reported that Samsung is in talks to integrate Perplexitys search technology into its devices. The deal would not only preload the Perplexity app onto Samsung phones, but also embed its search features directly into Samsungs web browser and virtual assistant, Bixby. Back in 2023, Perplexity looked like a frontrunner in AI searchbeating OpenAI and Google to the punch in crawling the live web. But the tech giants have since caught up, with ChatGPT and Gemini now offering similar capabilities. Could a high-profile partnership with Samsung be the boost Perplexity needs to reclaim its edge? Can Perplexity find a home? In its current form, Perplexity exists in a functional silo. The answer engine is primarily accessed through its stand-alone website or app, with no natural integration into users daily workflows. In other words, people have to seek it out. Now that its web-crawling technology is being replicated across competing chatbots, some users may no longer see a reason to choose Perplexity on its own. Its main value proposition under the Pro subscription is access to other companies LLMs, like GPT-4o and Claude 3.5. (Perplexity declined to comment for this story.) Integrating a chatbot into the users workflow is key to driving engagement. Google has embedded Gemini across nearly all of its products, from search to email. As a result, the Gemini app now boasts 400 million-plus monthly active users. Meta has taken an even more aggressive approach, integrating its AI into social apps and placing Meta AI above search. According to CEO Mark Zuckerberg, Meta AI now has more than 1 billion monthly active users. Other AI companies are embedding their models more subtly. While Apples Siri can now access ChatGPT, OpenAIs greatest reach comes from LLM licensing. Users dont just interact with GPT through ChatGPT, theyre engaging with it across dozens of third-party apps built on its technology. The same is true for Anthropic, which also licenses its models. Perplexity, by contrast, has limited back-end integrations via its API, and for the average user, encounters with its tech are still rare. Thats why a deal with Samsung would be a major step forward. A hardware integration would give Perplexity a critical new point of access. Meanwhile, Samsung has invested heavily in its Galaxy AI suite. Gemini is currently the default AI assistant for Samsung’s 1 billion-plus smartphone usersraising questions of whether Perplexity will displace or work alongside Google’s chatbot. (Samsung did not respond to Fast Companys request for comment.) Perplexitys position in the AI race Perplexity is still growing. While the company doesnt disclose revenue or user numbers, it claims users now generate more than 650 million queries per monthup from 400 million less than a year ago. Although some reports suggest that Perplexitys growth has come at a high cost, the company disputes those figures. Still, Perplexity has a lot to prove. It reached unicorn status in 2024 after raising $62.7 million at a $1.04 billion valuation. That valuation has reportedly ballooned to $14 billion in its current fundraising round. Meanwhile, the company is said to be generating less than $100 million in annual recurring revenue, according to CNBC. To stay competitive against imitators, Perplexity needs a more direct path to users. A deal with Samsung could provide exactly that.
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