ResidentialBusiness

Vice-president Luis de Guindos says Washington’s volatile trade policies and reduced co-operation also threaten stabilityView the full article

Curiosity is one of the most consequential forces in human history. Every scientific breakthrough, technological leap, and cultural advance begins not with knowledge, but the desire to know. At its core, curiosity drives us to close the gap between what we know and what we want to know, a cognitive itch triggered by uncertainty and resolved through learning and the pursuit of meaning. Curiosity as an evolutionary advantage Early humans who explored their environments, experimented with tools, and learned from novel stimuli were more likely to secure resources, avoid threats, and pass on their genes. As a result, curiosity became embedded in our biology, reinforced by neural reward systems that make learning intrinsically pleasurable. In line, neuroscientific research shows that curiosity activates the brain’s dopaminergic pathways, the same circuits involved in motivation and reward, which explains the positive correlation between curiosity and impulsivity. When we encounter a gap in our knowledge, we experience a mild form of cognitive discomfort. Resolving that gap produces satisfaction, reinforcing future exploration. In that sense, curiosity is a built-in, biologically coded feedback loop for learning. But evolution also imposed constraints. Curiosity, like any adaptive trait, is beneficial only within limits. For example, there are many scenarios in which too much exploration could prove fatal. A hunter-gatherer wandering too far from their tribe risked encountering predators or hostile groups. In such contexts, restraint was adaptive. Curiosity had to be expressed with caution. This tension between exploration and exploitation remains with us today. We are wired both to seek novelty and to prefer predictability. The familiar is efficient but boring; the unknown is exciting but costly. History offers similar patterns. During periods of intellectual repression, such as the Inquisition, curiosity was actively punished, making individual inquiry dangerous. By contrast, the Enlightenment celebrated curiosity as a virtue, unleashing scientific and philosophical progress. The same underlying human drive manifested differently depending on cultural conditions. Curiosity, then, is universal, but its expression is highly variable. The paradox of AI: A triumph that threatens its own foundation Fast forward to the present, and we are witnessing one of humanity’s greatest achievements: artificial intelligence. The convergence of mathematics, computer science, and data has enabled machines to simulate aspects of human cognition. We have, in effect, built systems that can approximate, emulate, and even surpass our thinking. Even if AI stopped evolving tomorrow (which seems unlikely), its implications are already profound. AI can augment human capability, accelerate problem-solving, and democratize access to knowledge. It functions as a cognitive copilot, allowing individuals to perform tasks that once required entire teams. But every technological advance also carries unintended consequences. And in the case of AI, the risks are not just economic and ethical, but psychological, too. Specifically, AI threatens to erode curiosity. The erosion of curiosity in the age of artificial certainty Curiosity depends on uncertainty. It requires a gap between what we know and what we want to know. AI, by design, collapses that gap. When answers are instantly available, prepackaged, and delivered with confidence, the motivation to explore diminishes. Why struggle with a problem when a machine can solve it in seconds? Why engage in deep learning when surface-level understanding is sufficient to get by? This is what I have described elsewhere as “artificial certainty.” AI does not just provide answers; it creates the illusion that we understand them. The output is coherent, fluent, and persuasive. But coherence is not comprehension. The result is a shift from active to passive cognition. We consume knowledge rather than generate it. We outsource thinking rather than exercise it. A useful analogy is physical fitness. Imagine a world where machines do all the lifting for you. Your muscles would atrophy. The same applies to the mind. Curiosity is a mental muscle, and like any muscle, it weakens with disuse. In this sense, AI is the equivalent of a microwave for ideas. It delivers fast, convenient results, but often at the expense of depth and craftsmanship. We move from “slow thinking,” which is effortful and reflective, to “fast consumption,” which is effortless but shallow. There is also a linguistic irony worth noting. “Deep learning,” once a human aspiration, is now primarily associated with machines. Meanwhile, human learning risks becoming increasingly superficial, if not dormant. To be sure, such concerns may ultimately prove overstated, as they often have in the past. Socrates, after all, warned that writing would erode memory, fearing that reliance on external tools would weaken internal capacities. Yet history also suggests that overcorrection is safer than complacency. There are, in fact, good reasons to be vigilant: When effort is removed from the learning process, engagement tends to decline; when answers are readily available, the incentive to question diminishes; and when cognition is outsourced too readily, the underlying skills can atrophy. The point is not to resist technological progress, but to ensure that convenience does not quietly displace the very mental habits that made such progress possible in the first place. What the science says about cultivating curiosity If curiosity is both essential and at risk, the obvious question is: Can it be developed? The answer is yes, but not in the simplistic way often suggested. Curiosity is influenced by both stable traits and situational factors. While some individuals are naturally more curious than others, environments and habits play a critical role. Before diving into what to do, it is worth pausing on a more basic question: How curious are you, really? The first step is a proper self-assessment. Not the flattering version you might hold of yourself, but a more objective view grounded in data and external feedback. Curiosity is closely linked to well-established personality traits, particularly openness to experience, one of the Big Five, which captures intellectual curiosity, imagination, and a preference for novelty. Science-based assessments can provide a reliable baseline here. So can 360-degree feedback, which often reveals a gap between how curious we think we are and how we are experienced by others. Even informal input from colleagues, friends, or mentors can be illuminating, especially when it highlights whether you ask thoughtful questions, challenge assumptions, or genuinely engage with new ideas. Equally important is specificity. Curiosity is not a uniform trait. People are rarely equally curious about everything. Reflect on where your curiosity naturally shows up and where it does not. You may be deeply inquisitive about ideas but indifferent to people, or fascinated by technology but incurious about history, culture, or opposing viewpoints. Mapping these patterns matters, because developing curiosity is not about becoming universally interested in everything. It is about understanding your blind spots and deliberately expanding into areas where your instinct is to disengage. First, intrinsic motivation matters. Studies grounded in self-determination theory show that curiosity flourishes when individuals feel autonomous, competent, and connected to others. In practical terms, this means people are more curious when they pursue topics that genuinely interest them, rather than those imposed externally. The implication for organizations is clear: forced learning rarely produces genuine curiosity. Second, exposure to novelty is key. Curiosity thrives on diversity of input. Interacting with people from different backgrounds, disciplines, and perspectives increases the likelihood of encountering information gaps. This is why interdisciplinary environments are often more innovative. They create friction between ideas. Third, habits of reflection enhance curiosity. Research on learning and memory suggests that active engagement, such as writing, teaching, or debating, deepens understanding and sustains curiosity. Passive consumption, by contrast, leads to the illusion of knowledge without real insight. Fourth, time allocation matters. Curiosity requires cognitive space. In environments dominated by urgency and efficiency, there is little room for exploration. Scheduling time for reading, thinking, and unstructured inquiry is not a luxury; it is a necessity. Fifth, tolerance for uncertainty is crucial. Individuals with a high need for cognitive closure prefer quick answers and are less likely to engage in open-ended exploration. Developing comfort with ambiguity, through practices such as Socratic questioning or deliberate exposure to complex problems, can enhance curiosity. Finally, there is evidence that curiosity can be trained through small behavioral interventions. For example, prompting individuals to generate questions before receiving answers increases engagement and retention. Similarly, framing tasks as puzzles or challenges can activate curiosity-driven motivation. These findings align with the broader argument that curiosity is not a fixed trait but a dynamic capability shaped by both internal and external factors. The role of leaders in modeling curiosity While individual strategies matter, curiosity is ultimately a social phenomenon. It is shaped, amplified, or suppressed by cultural norms. From early childhood, curiosity is not simply an individual trait but a product of developmental context. Parents, teachers, and early environmental experiences play a decisive role in shaping how, and whether, curiosity endures into adulthood. Research in developmental psychology shows that children whose caregivers respond contingently to their questions, encourage exploration, and tolerate uncertainty tend to develop higher levels of intrinsic curiosity. Conversely, environments that emphasize compliance, correct answers, and performance over inquiry can suppress exploratory behavior over time. Educational studies also find that classroom climates prioritizing rote learning and standardized outcomes often erode students’ natural inquisitiveness, even when baseline curiosity is high. Longitudinal evidence suggests that these early patterns persist, shaping adult tendencies toward intellectual risk-taking, openness, and lifelong learning. In short, curiosity is cultivated or constrained early, but its trajectory can be reinforced or reversed later, especially through social and organizational contexts. This is where leadership becomes critical. Leaders set the tone for what is valued. If they prioritize certainty, speed, and efficiency above all else, curiosity will decline. Employees will learn to avoid questions, minimize exploration, and focus on immediate outputs. Conversely, leaders who model curiosity create environments where inquiry is rewarded. This does not mean celebrating randomness or distraction. It means demonstrating intellectual humility, asking better questions, and showing a willingness to challenge assumptions. One of the most powerful signals a leader can send is admitting what they do not know. This reduces the perceived cost of ignorance and encourages others to engage in learning. It also counteracts overconfidence, which is one of the main barriers to curiosity. Leaders can also design systems that embed curiosity into workflows. This includes allocating time for experimentation, encouraging cross-functional collaboration, and measuring not just outcomes but learning processes. Importantly, curiosity must be linked to performance. It is not about asking more questions for their own sake, but about asking better questions that lead to better decisions. In the age of AI, this becomes even more important. As machines take over routine cognitive tasks, the human advantage shifts to areas that require judgment, interpretation, and creativity. These are all downstream of curiosity. But judgment without experience is meaningless. AI can simulate answers, but it cannot substitute for the depth that comes from actually engaging with the world. There is a difference between consuming a microwaved meal and cooking one from scratch, sourcing ingredients, understanding how they interact, and adjusting along the way. The former is efficient and convenient; the latter builds intuition, tacit knowledge, and real expertise. In the same way, relying on AI-generated outputs without cultivating firsthand learning experiences produces a thin version of competence, what might be called artificial understanding. Curiosity, when acted upon, pushes us into those richer experiences that give judgment its substance and make our thinking genuinely our own. Curiosity as a strategic imperative The rise of AI has not just expanded access to information; it has quietly eroded the premium once attached to possessing it. When virtually all answers are instant, abundant, and convincingly packaged, the differentiator is no longer what you know, but how you engage with what can be known. In that sense, the economics of expertise are shifting. Knowledge, at least in its most accessible forms, is becoming commoditized, while the capacity to interrogate, refine, and build on that knowledge is becoming scarcer and more valuable. This is where curiosity earns its value, not as a soft or “nice to have” trait, but as the underlying mechanism that sustains learning over time. Without curiosity, the risk is not ignorance, but something more insidious: the illusion of understanding. AI can generate coherent explanations, summarize complexity, and produce plausible insights at scale. But unless these outputs are met with questioning, skepticism, and a desire to go beyond what is given, they are unlikely to translate into genuine insight or better decisions. The danger, then, is not that machines will think for us, but that we will gradually outsource the very effort required to think well, confusing fluency with depth and access with mastery. This places a different kind of demand on individuals and organizations. The task is no longer simply to adopt AI tools or increase their usage, but to integrate them in ways that augment rather than atrophy human judgment. At the individual level, this implies a degree of intentionality that is often underestimated: cultivating habits that prioritize inquiry over convenience, depth over speed, and exploration over closure. At the organizational level, it requires more than rhetoric about innovation. It calls for environments where questioning is not penalized by the pressures of efficiency, and where time spent exploring is not automatically seen as time wasted. And at the leadership level, it demands a visible commitment to curiosity as a norm, expressed less through slogans and more through behavior: the questions senior exeuctives ask, the uncertainty they tolerate, and the assumptions they are willing to revisit, will all shape the organization’s level of curiosity and appetite for learning. There is an obvious irony here. The more capable our machines become at producing answers, the more valuable it becomes to remain interested in the questions. This is not a nostalgic defense of human uniqueness, but a pragmatic recognition of where advantage now lies. In a world where everyone has access to the same tools, and AI becomes as ubiquitous as smartphones, Wi-Fi, or electricity, the differentiating factor shifts to how those tools are used, and that, in turn, depends on the quality of human curiosity brought to bear on them. Seen in this light, curiosity becomes a strategic necessity, one that shapes not only how individuals learn, but how organizations adapt and compete in an environment where knowing is easy, but understanding remains hard, and is quietly becoming a niche pursuit. View the full article

As 7.4 million Americans sit unemployed, the path to employment has completely changed. Amid fake listings, AI filtering of candidates and widening talent pools, job seekers believe that they’re competing against a hiring ecosystem that penalizes honesty and rewards perception. The result? A hiring environment where the signals employers have traditionally relied on to evaluate candidates have become deeply unreliable. Now, both sides are operating with diminishing trust in each other. What’s Driving the Deception? Hiring today is not facing a character problem, but a structural one. When candidates believe that presenting themselves accurately will cost them a job offer, the rational response is to become the person they think the employer is looking for. But when this approach becomes standard, those who still choose to tell the truth take on an “honesty tax,” the systemic disadvantage honest candidates face when exaggeration becomes the market norm. GCheck’s Trust in Hiring Report revealed that 93% of job seekers have lied or embellished their experience during the hiring process, while 60% do not believe they would have been hired had they presented their qualifications more accurately. This is beyond a confession—it’s a market signal. Part of what drives this dynamic is opacity on the employer side. When candidates do not know what will be verified, they assume the answer is minimal, and they calibrate their self-presentation accordingly. In fact, GCheck found that although 88% of job seekers believe misrepresentation puts businesses at risk, 53% assumed employers wouldn’t verify their claims and only about a quarter (26%) report ever being caught lying or exaggerating. Verification that is invisible to candidates is not a deterrent. It is permission. And thanks to artificial intelligence, candidates can disguise their true skills and identity almost instantaneously. AI Accelerates Dishonesty in Hiring LinkedIn’s 2025 Work Change Report estimates that 70% of the skills used in most jobs will change by 2030, driven largely by AI. When job seekers navigate a market where the definition of “qualified” is constantly shifting, the pressure to appear more capable than they are significantly intensifies. AI has not created that pressure, but it has handed candidates sophisticated tools to act on it at every stage of the hiring process. Employer concerns have moved beyond job seekers’ using AI to compile resumes or assist with writing. Now, the degree to which AI has migrated into live interviews and assessments is worrisome. GCheck found that 61% of candidates have used AI to rehearse interview answers until they sounded more impressive than authentic, and 25% reported deploying an AI avatar in place of their own face during a virtual interview. ​ The result is a hiring process where trust is eroding on both sides. On one hand, candidates feel pressure to optimize and automate their performance in a highly mediated, virtual environment; on the other, employers struggle to assess who is genuinely behind the screen. When interviews are increasingly remote, scripted and technology driven, the lines between preparation and performance become blurred. This highlights how broken and transactional the modern hiring process has become. There’s also an emerging phenomenon of systematic embellishment, distortion or fabrication of professional qualifications across resumes, interviews, and references as a deliberate competitive strategy driven by market pressure and weak verification expectations. It’s been dubbed “careerfishing,” and it’s no longer the behavior of a fringe group. What Employers Must Do to Rebuild Trust Rebuilding trust in hiring is not only a technology problem, but also a standards and transparency issue. Employers who treat verification as a confidential back-end process get exactly what opacity produces: candidates who assume they can game the system, largely because they can. Three leadership-level shifts matter most here: Make verification standards visible. Communicate what will be checked before a candidate applies. Transparency disrupts embellishment at its source, not after the offer. The FTC’s guidance on employment background checks under the FCRA already mandates disclosure at specific stages. Moving that clarity upstream changes candidate behavior earlier in the process in measurable ways. For example, candidates who know credentials or work samples will be actually verified are less likely to exaggerate or rely on AI-generated materials they cannot defend later. Make screening decisions reviewable by a person. Candidates who know a human will review findings, not only an algorithm, engage with the process more honestly. Make verification proportionate to actual risk. Applying the same screening depth to every role signals to candidates that the process is performative. Calibrating scope to genuine role risk makes verification more credible, more defensible, and more likely to deter the embellishment it is meant to catch. In recent years, hiring integrity has evolved from a checkbox exercise into a strategic priority. When AI-driven careerfishing corrupts the foundational data a company uses to build its workforce, the damage surfaces in performance gaps. The goal is not to catch more people lying. The goal is to build a hiring environment where honesty carries a genuine advantage rather than a competitive penalty. When employers operate transparently and verify consistently, they stop performing diligence and start practicing it. That distinction is what separates organizations that attract trustworthy people from those that inadvertently select for the most convincing ones. View the full article

The outgoing chair has made some mistakes, but his decision to stand up to The President was heroicView the full article

Chinese territory enjoys surge of investment from mainland as wealthy spread assets across different jurisdictionsView the full article

Andrius Kubilius pushes for governments to open weapons stockpiles to Ukraine View the full article

Despite $17bn in pledges, organisation is stuck in limbo with no money flowing to projects in GazaView the full article

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The technology opens the door for smaller, well-funded challengers to take market share from the Big Four and others View the full article

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Albert Manifold’s behaviour and use of personal devices cited as factors in his removal over conduct and governance concernsView the full article

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Foreign Office announces measures against Huobi among several entities it says helped Russia evade economic pressureView the full article

Last weekend, when Puck announced that the sustainable fashion startup Everlane had been acquired by the Chinese ultra fast fashion retailer Shein, it sent shockwaves throughout the fashion world. Michael Preysman, who founded Everlane in 2011, was just as shocked. “I found out the same time as everyone else,” he said in a LinkedIn post a week ago. “I’m not involved with the company anymore, and like many, am still digesting the news.” Well, Preysman is done digesting. And it seems that he’s ready to do something about it. Preysman just announced stillradical.com, a new venture that we know little about other than the bare bones website it launched with. The website lays out the new vision with brevity: “I started Everlane in 2011. Last week, the current management team sold it to Shein. So we’re starting over. Same principles, but a new take. And this time: no venture capital, no private equity.” The site says you can learn more by signing up for a waitlist. (Preysman did not respond to a request for comment.) Preysman launched Everlane when he was in his mid-20s, after starting his career in finance. His vision was to sell high quality products directly to customers online, without the markup of middlemen like department stores. This helped kickstart the direct-to-consumer movement that dominated the 2010s, producing brands like Away, Warby Parker, Allbirds, and Glossier. To fuel its growth, Everlane took an undisclosed amount of venture capital. A few years in, Preysman turned his attention to the human and environmental impact of the fashion industry. Everlane promised to eradicate virgin plastic from its supply chain, and showed customers inside the factories they used, to highlight how it was paying attention to the working conditions of laborers. All of this was good for business. By 2016, Everlane was valued at $250 million, although it was unclear whether it had ever become profitable. In recent years, its growth slowed. It went through two rounds of layoffs, once during the pandemic and then again in 2023. L. Catterton—the venture capital wing of the luxury conglomerate LVMH—bought a majority stake in Everlane in 2020. Shortly after, Preysman left the company to launch a new supplements brand called Magna in 2024. For many, Everlane’s acquisition by Shein was a disappointing final chapter for a company that stood for optimism and ethics. Clearly, Preysman felt the same way about it. This new business suggests that he hasn’t given up on the idea of sustainable fashion. However, he’s realized that venture capital is not the right tool for launching an apparel business. It will be fascinating to see what lessons Preysman has taken from the rise and fall of Everlane, and how we plans to build his new company differently. View the full article

Victory of flawed Senate candidate Ken Paxton could cement the president’s hold on the party View the full article