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As researchers approach the front doors of Oxfords new Life and Mind Building (LaMB), theyre greeted with a towering concrete facade, rendered with a rippling surface effect. What first appears to be a mere stylistic choice actually encodes something more special: Each of the concretes waves and dips is derived from the brain scan of an Oxford researcher. Designed by the architecture firm NBBJ, the LaMB is a massive, 269,000-square-foot space that brings together two departments: experimental psychology, which studies the human brain and how it operates; and biology, which encompasses both zoology (animal studies) and plant sciences. When it opened last October after four years of construction, it became the largest facility on the historic universitys campus. [Photo: Ty Cole/NBBJ] The LaMBs facade is visually striking, but it also embodies a few clever ways that NBBJ is reimagining what a university lab building can be. The structure is built to account for the natural path of the sun, capture energy using solar panels, and use advanced cladding technology to lessen its environmental impact. In short, its a vision of a lab thats better for both its staffers and the planet. [Photo: Ty Cole/NBBJ] A brain wave hidden in plain sight When visitors look up at the LaMB, theyre literally observing someones positive thoughteven if they dont know it. Darius Umrigar, a principal architect at NBBJ and the lead project designer of LaMB, says one of his teams main priorities was to design with longevity in mind, given that Oxford itself is nearly 1,000 years old. That meant choosing durable materials (concrete, stone, and metal) to make up the facade. At the same time, they wanted to ensure that the building would have aesthetic interest and fit within the existing campus. [Photo: Richard Chivers/NBBJ] The solution would need to be a design that works with the buildings thick concrete exterior and can withstand the environment for decades. [Photo: Richard Chivers/NBBJ] During that process, we talked to the head of experimental psychology, Umrigar says. They do a lot of brain scanning, both voluntarily and in terms of their approach to research and treatment. When we were considering the design, we were looking at how it would weather well and maintain its beauty without needing to be maintained. They wanted a texture for the buildings cladding that wasnt just flat concrete, he adds. Through this discussion, one student volunteered her own brain scan to serve as the basis of a potential decorative feature. That student was Sage Boettcher, whos now a career development research fellow in the department of experimental psychology. A scan of her brain was taken while she actively envisioned the future of the LaMB lab. From there, the NBBJ team isolated a two-second blip of the recording, resulting in a distinct sinusoidal wave pattern of dips and curves. Those rippling gestures were then carved into various stone slabs, which appear at intervals across the buildings exterior. What we try to do with NBBJ is to not leave a legacy that dates the building, but look at using materials in the truest form and balance that with the budget we’ve got to work with, Umrigar says. I think the harmony of simple, quality materials that will weather well and stand the test of time is certainly the key to success. [Photo: Richard Chivers/NBBJ] Labs are a major energy suckthe LaMB uses clever design to combat that The LaMBs facade is designed to be beautifu, but it also serves a greater purpose for the environment. Traditionally, labs are a major energy suck. According to a 2019 study, the combined emissions of hospitals and labs account for 4.4% of the worlds total greenhouse gas emissions. (For context, the study found that labs at Harvard accounted for nearly 44% of the universitys energy use, yet only 20% of its total space.) According to the engineering consultancy Hoare Lea, which worked with NBBJ on the LaMB, labs are typically expected to consume three to five times as much energy as a traditional office. Some estimates put that figure even higher, at around a factor of 10. [Photo: Richard Chivers/NBBJ] The main reason labs drain so much energy is their intense climate control demands. Maintaining the integrity of the many experiments that take place every day means labs need 24/7 systems keeping their air filtered, moving at the correct volume, and tuned to specific humidity and temperature settings. All of these demands equal major energy inputsand, ultimately, high operational costs. One of the biggest ways that lab buildings can conserve energy, Umrigar says, is by simply preventing air from leaking out. For the NBBJ team, that meant creating an airtight cladding system. The final design includes an outer layer of precast concrete panels, a thick internal layer of thermal insulation, triple-glazed windows, and precision detailing to ensure that every nook and cranny is sealed against the outside world. [Photo: Richard Chivers/NBBJ] The LaMB is also carefully situated to maximize natural sunlight. A central atrium cutout allows in soft daylight without overheating the building, while harsher light is captured via a series of roof-mounted solar panels. Inside, air source heat pumps and adaptive ventilation (which uses special sensors to determine how and when to circulate air) cut down even further on wasted energy. In all, these clever design tactics mean that the LaMB emits about 40% less carbon than it would at baseline. It’s a great achievement for a very large lab building, and certainly probably pushing the boundaries of what the university has been able to do for science buildings of this type in the past, Umrigar says. It sets a new benchmark, I would say, not only for Oxford, but for many other universities looking to develop a research facility.
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A new book by a former Deloitte executive turned workplace well-being expert argues exactly that In her new book Hope Is the Strategy, Jen Fisher, an expert on workplace well-being and human sustainability, makes a clear and timely case that hope isn’t a soft skill or a leadership afterthought; it’s a practical, learnable approach to navigating uncertainty and building healthier, more resilient organizations. In the following excerpt, Fisher draws on her personal experience grappling with burnout, as well as her research on well-being, leadership, and corporate culture, to reframe hope as something we can all learn and implement for ourselves and those we work with. We’ve long misunderstood hope in the workplace. We’ve treated it as wishful thinkinga nice-to-have feeling that emerges when things are going well. But research from psychologist C.R. Snyder reveals something far more powerful: Hope is a cognitive process with three essential components: goals (what we want to achieve), pathways (our ability to identify routes to those goals), and agency (our belief that we can pursue those paths). This isn’t passive optimism; it’s an active strategy for navigating uncertainty and driving meaningful change. After my own experience with burnout, I discovered that hope isn’t what you turn to after strength failshope is the strength we’ve been looking for all along. It’s not the light at the end of the tunnel; it’s the torch we need to lead others through it. And when organizations embed hope into their leadership practices and culture, they unlock something remarkable: the capacity to transform not just how people feel about work, but what they can actually accomplish together. As more organizations prioritize helping their employees become healthier, more skilled for the future, and connected to a sense of purpose and belonging, they have an opportunity to instill hope in leadership and encourage it in workers. A roadmap for the future A leader who has hope can map out a path for an employee, offering a solid roadmap rather than an empty promise. They might say, “I can’t promise you complete job security, but I can provide you with the skills that will make you attractive in the job market.” That, in turn, helps foster hope in the worker, because they know that they’ll have more tools in their success toolkit, no matter what the future holds. That’s not just a win for the individual, but for the group. An organization (of any typeit could also be a community, or a family) filled with people tapped into their meaning and purpose is stronger than one made up of disengaged, unhealthy, and unhappy people. In fact, hope is a strategy for a variety of prevalent workplace problems: It can improve mental well-being and stress management; it can drive action and reduce catastrophic thinking; and it can help overcome the disengagement crisis at work. What’s more, hope will support our transition to a more human-centered workplace as AI takes on the more mundane, tactical aspects of work. Creating new ripples from leadership on down is possibleand as with the negative ones, it starts with modeling behaviors to set the tone for your team and your peers. That is, modeling the sustainable work behaviors and values that will drive purpose and well-being. Here are four examples: 1. Get clear on what your own boundaries are If you’re following someone else’s vision of success instead of your own, you’re going to end up miserable and probably burned out. So take that PTOreally. The company will not crumble without you. And don’t answer that email at midnightreply in the morning, during work hours. A leader who actually sets healthy boundaries and lives by them gives employees permission to do the same. As I reevaluated the role that work played in my life, I set my own new boundaries. I got clear on what my definition of success was, instead of allowing the external world to define that for me. And I brought hope into my life: I started each day with a set of “what if” questions, looking at the day ahead through the lens of possibility: What if this goes right? What if I do things this way? Then I’d end each day with reflection: How did it go? It helped me to see challenges as an opportunity for change. Here are some other daily practices I put in place, all of which I still follow today: Treat sleep as a nonnegotiable. I protect my eight hours like the business asset it actually is, recognizing that sleep isn’t a luxury but the foundation that makes everything else possible. Schedule humanity into the calendar. Not vague “personal time” but specific blocks for connections that make me human: dinner with my husband, phone calls with friends, reading fiction that has nothing to do with work. Incorporate daily recovery rituals. Three-minute breathing breaks between meetings, a proper lunch away from my desk, a brief walk outside to reset my nervous systemthese small moments of renewal prevent depletion from accumulating. Defend the calendar against the tyranny of urgency. Breakfast, lunch, dinner, exercise, and sleep aren’t just activities to fit around “real work”they comprise the immovable infrastructure that sustains my performance. Everything else has to work around them, not the other way around. 2. Embrace the unknown When we temporarily suspend our need for certainty, a different kind of productivity emerges. I call these my Possibility Days: Once a week, I grant myself permission to coexist with uncertainty. Instead of trying to control outcomes, I deliberately seek experiences with unknown results. I have conversations without preparing talking points. I explore ideas that seem impractical. I follow curiosity down rabbit holes without worrying where they lead. My most innovative solutions and deepest insights almost always trace back to these deliberate ventures into possibility thinking. 3. Walk the walk The old ways of leading through power and control are giving way to something more human, more hopeful, and more whole. The future of leadership isn’t just about what we doit’s about how we show up, how we hold space for both struggle and possibility, and how we cultivate well-being as a vital way of being. There’s this old thinking that we should check our feelings or emotions at work. It’s basically telling people: Don’t show up as who you truly are. When leaders normalize having no energy, no life, no nothing beyond work, it becomes not just accepted but expected. Emotions, whether they’re positive or negative, are really a sign of the things we care aboutand when we’re told not to bring emotions into the workplace, it stunts creativity, growth, innovation, connection, and understanding. The answer is simple: Show your emotions. Your employees look to you to set the pace, tone, and stakes of the team and the work being done. Be vulnerable and authentic about when you’ve made a mistake, when you said one thing and you did another, when you screwed up. Your actions show themthat decisions to support their own health and well-being and career growth aren’t going to be viewed negatively or make it seem like they’re less committed to their work. 4. Build teams grounded in trust True organizational and individual success depends on teams built on mutual trustteams that prioritize deep relationships alongside personal well-being. Trust-based teams require leaders who actively invite people to show up authentically and provide genuine support when they do. This means fostering psychological safety where team members feel confident giving honest feedback, taking calculated risks, learning from missteps, and growing from challenges rather than facing punishment for them. Organizations with the strongest well-being cultures maintain ongoing dialogue between leaders and team members. Within trust-based environments, people develop a growth-oriented perspective. Colleagues treat each other with genuine care and respect, creating workplaces rooted in kindness. This positive energy extends far beyond individual teams, helping organizations attract diverse talent, improve retention, spark innovation, and build lasting resilience.
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E-Commerce
When one of the founders of modern AI walks away from one of the worlds most powerful tech companies to start something new, the industry should pay attention. Yann LeCuns departure from Meta after more than a decade shaping its AI research is not just another leadership change. It highlights a deep intellectual rift about the future of artificial intelligence: whether we should continue scaling large language models (LLMs) or pursue systems that understand the world, not merely echo it. Who Yann LeCun is, and why it matters LeCun is a French American computer scientist widely acknowledged as one of the Godfathers of AI. Alongside Geoffrey Hinton and Yoshua Bengio, he received the 2018 Association for Computing Machinerys A.M. Turing Award for foundational work in deep learning. He joined Meta (then Facebook) in 2013 to build its AI research organization, eventually known as FAIR (Facebook/META Artificial Intelligence Research), a lab that tried to advance foundational tools such as PyTorch and contributed to early versions of Llama. Over the years, LeCun became a global figure in AI research, frequently arguing that current generative models, powerful as they are, do not constitute true intelligence. What led him to leave Meta LeCuns decision to depart, confirmed in late 2025, was shaped by both strategic and philosophical differences with Metas evolving AI focus. In 2025, Meta reorganized its AI efforts under Meta Superintelligence Labs, a division emphasizing rapid product development and aggressive scaling of generative systems. This reorganization consolidated research, product, infrastructure, and LLM initiatives under leadership distinct from LeCuns traditional domain. Within this new structure, LeCun reported not to a pure research leader, but to a product and commercialization-oriented chain of command, a sign of shifting priorities. But more important than that, theres a deep philosophical divergence: LeCun has been increasingly vocal that LLMs, the backbone of generative AI, including Metas Llama models, are limited. They predict text patterns, but they do not reason or understand the physical world in a meaningful way. Contemporary LLMs excel at surface-level mimicry, but lack robust causal reasoning, planning, and grounding in sensory experience. As he has said and written, LeCun believes LLMs are useful, but they are not a path to human-level intelligence. This tension was compounded by strategic reorganizations inside Meta, including workforce changes, budget reallocations, and a cultural shift toward short-term product cycles at the expense of long-term exploratory research. The big idea behind his new company LeCuns new venture is centered on alternative AI architectures that prioritize grounded understanding over language mimicry. While details remain scarce, some elements have emerged: The company will develop AI systems capable of real-world perception and reasoning, not merely text prediction. It will focus on world models, AI that understands environments through vision, causal interaction, and simulation rather than only statistical patterns in text. LeCun has suggested the goal is systems that understand the physical world, have persistent memory, can reason, and can plan complex actions. In LeCuns own framing, this is not a minor variation on todays AI: Its a fundamentally different learning paradigm that could unlock genuine machine reasoning. Although Meta founders and other insiders have not released official fundraising figures, multiple reports indicate that LeCun is in early talks with investors and that the venture is attracting atention precisely because of his reputation and vision. Why this matters for the future of AI LeCuns break with Meta points to a larger debate unfolding across the AI industry. LLMs versus world models:LLMs have dominated public attention and corporate strategy because they are powerful, commercially viable, and increasingly useful. But there is growing recognition, echoed by researchers like LeCun, that understanding, planning, and physical reasoning will require architectures that go beyond text. Commercial urgency versus foundational science:Big Tech companies are understandably focused on shipping products and capturing market share. But foundational research, the kind that may not pay off for years, requires a different timeline and incentives structure. LeCuns exit underscores how those timelines can diverge. A new wave of AI innovation:If LeCuns new company succeeds in advancing world models at scale, it could reshape the AI landscape. We may see AI systems that not only generate text but also predict outcomes, make decisions in complex environments, and reason about cause and effect. This would have profound implications across industries, from robotics and autonomous systems to scientific research, climate modeling, and strategic decision-making. What it means for Meta and the industry Metas AI strategy increasingly looks short-term, shallow, and opportunistic, shaped less by a coherent research vision than by Mark Zuckerbergs highly personalistic leadership style. Just as the metaverse pivot burned tens of billions of dollars chasing a narrative before the technology or market was ready, Metas current AI push prioritizes speed, positioning, and headlines over deep, patient inquiry. In contrast, organizations like OpenAI, Google DeepMind, and Anthropic, whatever their flaws, remain anchored in long-horizon research agendas that treat foundational understanding as a prerequisite for durable advantage. Metas approach reflects a familiar pattern: abrupt strategic swings driven by executive conviction rather than epistemic rigor, where ambition substitutes for insight and scale is mistaken for progress. Yann LeCuns departure is less an anomaly than a predictable consequence of that model. But LeCuns departure is also a reminder that the AI field is not monolithic. Different visions of intelligence, whether generative language, embodied reasoning, or something in between, are competing for dominance. Corporations chasing short-term gains will always have a place in the ecosystem. But visionary research, the kind that might enable true understanding, may increasingly find its home in independent ventures, academic partnerships, and hybrid collaborations. A turning point in AI LeCuns decision to leave Meta and pursue his own vision is more than a career move. It is a signal: that the current generative AI paradigm, brilliant though it is, will not be the final word in artificial intelligence. For leaders in business and technology, the question is no longer whether AI will transform industries, its how it will evolve next. LeCuns new line of research is not unique: Other companies are following the same idea. And this idea might not just shape the future of AI researchit could define it.
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