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We often think of serendipity as lucka fortunate coincidence or a happy accident. But what if its something more intentional? What if serendipity is less about chance and more about conditions? Whether its a hallway conversation that sparks a billion-dollar idea or a side project that becomes your next calling, many of the most transformational moments in life and work are unplanned, but not uninvited. These moments happen when we build environments, both mental and physical, that are open to the unexpected. The question isnt whether serendipity exists. Its whether youre making space for it. The Case for Intentional Serendipity Take Steve Jobs. He famously credited a college calligraphy classan elective he took purely out of curiositywith inspiring the design of Apples iconic typography. At the time, the class had nothing to do with his career. But it ended up shaping the aesthetic identity of one of the most influential companies in history. Or consider the origin story of CRISPR. The revolutionary gene-editing tool began with a casual conference conversation between two scientists from different disciplines. Their impromptu exchange sparked a collaboration that led to one of the most important scientific breakthroughs of the 21st century. These werent just lucky accidents. They were the result of environments primed for discoveryspaces where curiosity, diversity, and ambiguity could coexist. Serendipity isnt magic; it is emergence, and you can design for it. In my work with senior leadership teams, Ive seen this firsthand. I once hosted an off-site where a brief side conversation during a break, completely off-agenda, led two leaders to uncover a shared experience that reshaped how they collaborated. What followed was a strategic pivot that the team had been struggling to make for months. It reminded me that the real breakthroughs often dont happen during scheduled agenda items; they happen between them. The key is creating the conditions where these moments can arise. A Framework for Creating Serendipity Orchestrating serendipity means increasing your exposure to diverse inputs, unexpected ideas, and interdisciplinary collisions. Heres how to make it happen: 1. Create Surface Area You cant bump into new ideas if youre stuck in the same lanes. Professionally, that might mean attending events outside your industry, joining cross-functional projects, or working from a new space, whether a coworking hub, a public library, or your favorite off-route coffee shop. Personally, try picking up a new hobby, joining a different kind of community, or reaching out to someone who sees the world differently than you do. Try this: Connect with someone whose work is completely unrelated to yours. Ask what theyre obsessed with and why. 2. Lead with Curiosity Serendipity doesnt reward certainty; it rewards openness. In organizations, that means creating cultures where good questions matter more than fast answers. Replace Why are we doing this? with What else might be possible? Encourage exploration, tangents, and thoughtful wandering. Individually, follow your fascinations. Read outside your domain. Ask better questions at dinner parties. Let your interests lead you, even if you dont yet know where theyre going. Start a curiosity stack, a running list of topics, people, and ideas that fascinate you. Just follow the breadcrumbs and see where they lead you. 3. Engineer Cross-Pollination Innovation loves unlikely collisions. Inside companies, dont wait for an annual retreat to mix disciplines. Create micro-moments of exchange like shared meals, rotating pair sessions, or jam sessions across departments. Outside of work, host a gathering where not everyone knows each other. Invite people across industries, cultures, and generations. Try organizing a 5-5-5 Dinner: five people, five perspectives, and five curated prompts. See what emerges when diverse minds meet around a shared table. In an era of accelerating complexity, innovation doesnt come from working harder; it comes from thinking differently, which requires exposure to new perspectives. A Harvard Business School study found that teams with greater cognitive diversity solve problems faster than more homogeneous ones. Similarly, the World Economic Forum identifies curiosity, creativity, and cross-domain collaboration as top future-of-work skills. Put simply, the ability to generate new value depends on your ability to connect unexpected dots, and serendipity is the connector. Build Your Serendipity Habit The most extraordinary breakthroughs often begin in ordinary momentsbut only if youve built a system that invites those moments in. This week, try one of these: Reconnect with someone in a different field youve been meaning to reach out to. Sign up for a class or event that has nothing to do with your job. Start a conversation with a colleague about something unrelated to work and follow where it leads. Serendipity isnt a fluke; its something you can design. When you embrace curiosity, invite collisions, and stay open to the unknown, you increase the odds that something meaningful and unexpected will find its way to you. The next big thing in your work or life may already be comingyou just need to be ready to meet it.
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Earlier this year, a robot completed a half-marathon in Beijing in just under 2 hours and 40 minutes. Thats slower than the human winner, who clocked in at just over an hourbut its still a remarkable feat. Many recreational runners would be proud of that time. The robot kept its pace for more than 13 miles (21 kilometers). But it didnt do so on a single charge. Along the way, the robot had to stop and have its batteries swapped three times. That detail, while easy to overlook, speaks volumes about a deeper challenge in robotics: energy. Modern robots can move with incredible agility, mimicking animal locomotion and executing complex tasks with mechanical precision. In many ways, they rival biology in coordination and efficiency. But when it comes to endurance, robots still fall short. They dont tire from exertionthey simply run out of power. As a robotics researcher focused on energy systems, I study this challenge closely. How can researchers give robots the staying power of living creaturesand why are we still so far from that goal? Though most robotics research into the energy problem has focused on better batteries, there is another possibility: Build robots that eat. Robots move well but run out of steam Modern robots are remarkably good at moving. Thanks to decades of research in biomechanics, motor control, and actuation, machines such as Boston Dynamics Spot and Atlas can walk, run, and climb with an agility that once seemed out of reach. In some cases, their motors are even more efficient than animal muscles. But endurance is another matter. Spot, for example, can operate for just 90 minutes on a full charge. After that, it needs nearly an hour to recharge. These runtimes are a far cry from the eight- to 12-hour shifts expected of human workersor the multiday endurance of sled dogs. The issue isnt how robots moveits how they store energy. Most mobile robots today use lithium-ion batteries, the same type found in smartphones and electric cars. These batteries are reliable and widely available, but their performance improves at a slow pace: Each year new lithium-ion batteries are about 7% better than the previous generation. At that rate, it would take a full decade to merely double a robots runtime. Animals store energy in fat, which is extraordinarily energy dense: nearly 9 kilowatt-hours per kilogram. Thats about 68 kWh total in a sled dog, similar to the energy in a fully charged Tesla Model 3. Lithium-ion batteries, by contrast, store just a fraction of that, about 0.25 kilowatt-hours per kilogram. Even with highly efficient motors, a robot like Spot would need a battery dozens of times more powerful than todays to match the endurance of a sled dog. And recharging isnt always an option. In disaster zones or remote fields, or on long-duration missions, a wall outlet or a spare battery might be nowhere in sight. In some cases, robot designers can add more batteries. But more batteries mean more weight, which increases the energy required to move. In highly mobile robots, theres a careful balance between payload, performance, and endurance. For Spot, for example, the battery already makes up 16% of its weight. Some robots have used solar panels, and in theory these could extend runtime, especially for low-power tasks or in bright, sunny environments. But in practice, solar power delivers very little power relative to what mobile robots need to walk, run, or fly at practical speeds. Thats why energy harvesting like solar panels remains a niche solution today, better suited for stationary or ultra-low-power robots. Why it matters These arent just technical limitations. They define what robots can do. A rescue robot with a 45-minute battery might not last long enough to complete a search. A farm robot that pauses to recharge every hour cant harvest crops in time. Even in warehouses or hospitals, short runtimes add complexity and cost. If robots are to play meaningful roles in society assisting the elderly, exploring hazardous environments, and working alongside humans, they need the endurance to stay active for hours, not minutes. New battery chemistries such as lithium-sulfur and metal-air offer a more promising path forward. These systems have much higher theoretical energy densities than todays lithium-ion cells. Some approach levels seen in animal fat. When paired with actuators that efficiently convert electrical energy from the battery to mechanical work, they could enable robots to match or even exceed the endurance of animals with low body fat. But even these next-generation batteries have limitations. Many are difficult to recharge, degrade over time, or face engineering hurdles in real-world systems. Fast charging can help reduce downtime. Some emerging batteries can recharge in minutes rather than hours. But there are trade-offs. Fast charging strains battery life, increases heat, and often requires heavy, high-power charging infrastructure. Even with improvements, a fast-charging robot still needs to stop frequently. In environments without access to grid power, this doesnt solve the core problem of limited onboard energy. Thats why researchers are exploring alternatives such as refueling robots with metal or chemical fuelsmuch like animals eatto bypass the limits of electrical charging altogether. An alternative: Robotic metabolism In nature, animals dont recharge; they eat. Food is converted into energy through digestion, circulation, and respiration. Fat stores that energy, blood moves it, and muscles use it. Future robots could follow a similar blueprint with synthetic metabolisms. Some researchers are building systems that let robots digest metal or chemical fuels and breathe oxygen. For example, synthetic stomach-like chemical reactors could convert high-energy materials such as aluminum into electricity. This builds on the many advances in robot autonomy, where robots can sense objects in a room and navigate to pick them up, but here they would be picking up energy sources. Other researchers are developing fluid-based energy systems that circulate like blood. One early example, a robotic fish, tripled its energy density by using a multifunctional fluid instead of a standard lithium-ion battery. That single design shift delivered the equivalent of 16 years of battery improvements, not through newchemistry but through a more bioinspired approach. These systems could allow robots to operate for much longer stretches of time, drawing energy from materials that store far more energy than todays batteries. In animals, the energy system does more than just provide energy. Blood helps regulate temperature, deliver hormones, fight infections, and repair wounds. Synthetic metabolisms could do the same. Future robots might manage heat using circulating fluids or might heal themselves using stored or digested materials. Instead of a central battery pack, energy could be stored throughout the body in limbs, joints and soft, tissue-like components. This approach could lead to machines that arent just longer-lasting but are more adaptable, resilient, and lifelike. The bottom line Todays robots can leap and sprint like animals, but they cant go the distance. Their bodies are fast and their minds are improving, but their energy systems havent caught up. If robots are going to work alongside humans in meaningful ways, well need to give them more than intelligence and agility. Well need to give them endurance. James Pikul is an associate professor of mechanical engineering at the University of Wisconsin-Madison. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Artificial intelligence began as a quest to simulate the human brain. Is it now in the process of transforming the human brains role in daily life? The Industrial Revolution diminished the need for manual labor. As someone who researches the application of AI in international business, I cant help but wonder whether it is spurring a cognitive revolution, obviating the need for certain cognitive processes as it reshapes how students, workers, and artists write, design, and decide. Graphic designers use AI to quickly create a slate of potential logos for their clients. Marketers test how AI-generated customer profiles will respond to ad campaigns. Software engineers deploy AI coding assistants. Students wield AI to draft essays in record timeand teachers use similar tools to provide feedback. The economic and cultural implications are profound. What happens to the writer who no longer struggles with the perfect phrase, or the designer who no longer sketches dozens of variations before finding the right one? Will they become increasingly dependent on these cognitive prosthetics, similar to how using GPS diminishes navigation skills? And how can human creativity and critical thinking be preserved in an age of algorithmic abundance? Echoes of the Industrial Revolution Weve been here before. The Industrial Revolution replaced artisanal craftsmanship with mechanized production, enabling goods to be replicated and manufactured on a mass scale. Shoes, cars, and crops could be produced efficiently and uniformly. But products also became more bland, predictable, and stripped of individuality. Craftsmanship retreated to the margins, as a luxury or a form of resistance. Today, theres a similar risk with the automation of thought. Generative AI tempts users to conflate speed with quality, productivity with originality. The danger is not that AI will fail us, but that people will accept the mediocrity of its outputs as the norm. When everything is fast, frictionless, and good enough, theres the risk of losing the depth, nuance, and intellectual richness that define exceptional human work. The rise of algorithmic mediocrity Despite the name, AI doesnt actually think. Tools such as ChatGPT, Claude, and Gemini process massive volumes of human-created content, often scraped from the internet without context or permission. Their outputs are statistical predictions of what word or pixel is likely to follow based on patterns in data theyve processed. They are, in essence, mirrors that reflect collective human creative output back to usersrearranged and recombined, but fundamentally derivative. And this, in many ways, is precisely why they work so well. Consider the countless emails people write, the slide decks that strategy consultants prepare, and the advertisements that suffuse social media feeds. Much of this content follows predictable patterns and established formulas. It has been there before, in one form or the other. Generative AI excels at producing competent-sounding contentlists, summaries, press releases, advertisementsthat bears the signs of human creation without that spark of ingenuity. It thrives in contexts where the demand for originality is low and when good enough is, well, good enough. When AI sparksand stiflescreativity Yet, even in a world of formulaic content, AI can be surprisingly helpful. In one set of experiments, researchers tasked people with completing various creative challenges. They found that those who used generative AI produced ideas that were, on average, more creative, outperforming participants who used web searches or no aids at all. In other words, AI can, in fact, elevate baseline creative performance. However, further analysis revealed a critical trade-off: Reliance on AI systems for brainstorming significantly reduced the diversity of ideas produced, which is a crucial element for creative breakthroughs. The systems tend to converge toward a predictable middle rather than exploring unconventional possibilities at the edges. I wasnt surprised by these findings. My students and I have found that the outputs of generative AI systems are most closely aligned with the values and worldviews of wealthy, English-speaking nations. This inherent bias quite naturally constrains the diversity of ideas these systems can generate. More troubling still, brief interactions with AI systems can subtly reshape how people approach problems and imagine solutions. One set of experiments tasked participants with making medical diagnoses with the help of AI. However, the researchers designed the experiment so that AI would give some participants flawed suggestions. Even after those participants stopped using the AI tool, they tended to unconsciously adopt those biases and make errors in their own decisions. What begins as a convenient shortcut risks becoming a self-reinforcing loop of diminishing originalitynot because these tools produce objectively poor content, but because they quietly narrow the bandwidth of human creativity itself. Navigating the cognitive revolution True creativity, innovation, and research are not just probabilistic recombinations of past data. They require conceptual leaps, cross-disciplinary thinking, and real-world experience. These are qualities AI cannot replicate. It cannot invent the future. It can only remix the past. What AI generates may satisfy a short-term need: a quick summary, a plausible design, a passable script. But it rarely transforms, and genuine originality risks being drowned in a sea of algorithmic sameness. The challenge, then, isnt just technological. Its cultural. How can the rreplaceable value of human creativity be preserved amid this flood of synthetic content? The historical parallel with industrialization offers both caution and hope. Mechanization displaced many workers but also gave rise to new forms of labor, education, and prosperity. Similarly, while AI systems may automate some cognitive tasks, they may also open up new intellectual frontiers by simulating intellectual abilities. In doing so, they may take on creative responsibilities, such as inventing novel processes or developing criteria to evaluate their own outputs. This transformation is only at its early stages. Each new generation of AI models will produce outputs that once seemed like the purview of science fiction. The responsibility lies with professionals, educators, and policymakers to shape this cognitive revolution with intention. Will it lead to intellectual flourishing or dependency? To a renaissance of human creativity or its gradual obsolescence? The answer, for now, is up in the air. Wolfgang Messner is a clinical professor of international business at the University of South Carolina. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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