Corporate culture isnt built by policies. Its built by momentsthe unscripted experiences that catch us off guard, bring us closer, and quietly shape how we show up for one another.
But many efforts labeled culture-building, including onboarding programs, leadership retreats, and all-hands meetings, still feel like productivity theater: tightly scheduled and heavy on performance. Today, its worth asking whether that model has simply run its course.
Consider this: what if the future of culture-building isnt about managing people, but about designing experiences that allow people to feel something real together? What if awe, story, and shared creativity werent treated as indulgences, but as foundational elements of how trust, courage, and belonging actually form?
Beyond the Mission Statement
While leaders like to bring up the idea of team culture, few can describe what theirs feels like in practice. Thats because culture doesnt live in a mission statement or a values deck. It lives in the stories people tell when no one is watching. It lives in how they feel after a team gathering. It lives in the space between intention and lived experience.
The data reinforces this gap. Deloitte reports that only 23% of organizations believe their employees are strongly aligned with corporate purpose. Gallup finds that just two in ten employees feel connected to their companys culture on a daily basis.
These arent engagement or communication problems; they are failures of experience design. When culture is reduced to language and artifacts, it stays abstract. When its shaped through shared experience, it becomes something people carry with them.
Designing a Culture People Can Actually Feel
Imagine replacing a traditional all-hands meeting with a creative exercise in which each team member contributes a visual expression of what matters most to them at work. Or imagine a leadership offsite that trades breakout rooms for a story circle, where leaders share pivotal moments that shaped how they lead today. People may forget the fourth bullet on slide 37, but they remember the moment they felt genuinely seen. Thats where culture actually forms.
Across my work with teams and leaders ranging from early-stage companies to established organizations navigating change, the most durable cultural shifts dont come from tighter processes or clearer messaging. They come from intentionally designed experiences built around three elements humans have relied on for connection long before modern organizations existed: art, ritual, and awe. These lay the grounds for emotional experienceswhich can determine trust, risk-taking, and follow-through.
Art as a Medium for Meaning
When teams create something togetherwithout relying on wordshierarchies soften, safety increases, and unspoken dynamics surface naturally. Art invites play and perspective, two capacities most workplaces quietly suppress.
At a recent leadership offsite, I facilitated a collaborative art experience where each participant expressed a core value visually, without explanation. What emerged was more than a collective artwork; it was a shared mirror. People recognized one another in new ways. Long after the offsite ended, the exercise continued to shape conversations. Art creates space for truth to surface without requiring debate or performance.
Ritual as Emotional Architecture
Ritual has a way of slowing us down and signaling significance. Simple, intentional gesturesopening a meeting with a shared intention, closing an offsite with a moment of gratitude, marking transitions with presenceturn routine interactions into moments of coherence.
In my Campfires of Connection work, gatherings begin and end with ritual: lighting a fire, sharing a single word, or pausing together in silence. These gestures dont demand belief or explanation; they communicate something more fundamental: this moment matters.
One of my clients began opening weekly meetings with a 60-second pause and a single prompt: What are you bringing here today? Over time, that slight shift deepened trust more effectively than any formal team-building program. Ritual isnt soft; its the emotional structure. It creates the container in which change becomes possible.
Awe as a Catalyst for Connection
Modern workplaces are loud, fast, and cognitively overloaded. Many people arent disengaged because they dont care; theyre overstimulated and starved of wonder. Awe interrupts that pattern. It resets the nervous system and expands perspective.
In one of my facilitation sessions, participants were invited to sketch places from their childhood and share the stories behind them. The drawings were simple and imperfect, yet deeply personal. As each was revealed, the room changed. Colleagues who had known one another only through polished professional roles suddenly encountered one another as whole people with layered histories.
That collective pause created a sense of awe. These moments dont happen accidentally. Theyre carefully designed to allow people to encounter something beyond their roles. In environments driven by metrics and deadlines, awe reminds us why collaboration matters and why people choose to stay, contribute, and stretch together rather than simply comply.
When Culture-Building Falls Flat
To understand why this approach matters, it helps to consider the alternative. I once observed a leadership retreat that checked every conventional box. The agenda featured well-known speakers, the breakout sessions were smartly facilitated, and participants left entertained, informed, and exhausted. But within weeks, nothing had changed. The retreat generated momentum but not meaning.
What was missing wasnt effort; it was emotional resonance. There was no moment when people could set aside the performance of leadership and engage with one another more honestly. The experience was efficient, but forgettable.
Months later, a much smaller intervention with the same group, a single evening structured around reflection, had a disproportionate impact. Leaders spoke openly about uncertainty, named tensions they had been avoiding, and listened without trying to fix or impress. That evening reshaped how they worked together more than any previous retreat had. Culture doesnt shift because information is delivered; it shifts when people feel something together that changes how they see one another.
For leaders designing their next team gathering, the most useful questions may not be logistical at all. What do we want people to feel when they leave this room? What truth needs space to surface here? What has been rushed past that deserves reverence? What might become possible if we slowed down just enough to let meaning catch up?
The organizations people love working for arent those with the slickest branding or the most polished values decks. Theyre the ones where people leave a meeting or retreat feeling more alive, more trusted, and more willing to take risks together.
Our capacity to juggle several tasks at once is among the most important capabilities of the human cognitive system.
Just consider a typical day in the life of a modern human: you glance at your phone while waiting for coffee to brew, skim headlines while half-listening to a podcast, mentally rehearse a client pitch while walking your child to school, reply noted on Slack during a meeting while updating a slide deck, check your bank balance while standing in line, and, in a moment of entirely optional productivity theatre, scroll through a friends Facebook feed to see what their cat had for breakfast (admittedly, not the most important addition to our already heavy repertoire of multi-tasks).
If these familiar episodes of multitasking barely register as effort, it is because they have been absorbed into habit, woven into the fabric of daily life, quietly showing how often we coordinate competing goals, priorities, and impulses at once. For all the noise about AI agents, it is worth remembering that human agents remain remarkably capable.
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That said, generative AI and AI agents add yet another layer of temptation to multitask, and a respectable excuse for doing so. Now we can draft an email while an agent prepares slides, ask a chatbot to summarize a report while we skim LinkedIn, generate code while answering Slack, or prompt three models at once while half-editing a memo. This feels like augmented productivity, but often becomes cognitive diffusion or an increase in work intensity. As I illustrated in I, Human, when machines take over fragments of thinking, we become supervisors of many shallow streams rather than authors of one coherent argument. The result is not just intellectual sloppiness, but a steady erosion of focus, as attention shifts from solving a problem to managing tools that promise to solve it for us.
A bad rap
To be sure, multitasking tends to get a bad rap, especially among cognitive psychologists and behavioral scientists. This skepticism is well grounded. In a widely cited meta-analysis, researchers showed that alternating between tasks produces measurable switch costs in both speed and accuracy, even when tasks are simple. Subsequent research also found that heavy media multitaskers performed worse on tests of attention control and working memory, suggesting that frequent task-switching may erode the very cognitive filters that make focus possible. A more recent synthesis including examination of social media effects linked media multitasking during studying to significantly poorer academic outcomes. More recent neuroscientific evidence also shows that habitual multitasking is associated with reduced grey-matter density in regions linked to cognitive control, and some scholars have pointed out that multitasking deducts the equivalent of 10-IQ points from our performance and is therefore more debilitating than smoking weed (presumably minus the benefits or self-perceived creativity!).
Taken together, the evidence is rather compelling: multitasking is not a sign of superior efficiency but a tax on attention, trading depth for the comforting illusion of productivity. It makes us feel busy, sometimes even clever, yet especially for complex, analytical, or creative work it is usually worse than doing one thing well at a time, or learning to focus.
Supertaskers
And yet, that is not to say that we are all equally bad at multitasking. In fact, as in most areas of cognition, there are meaningful individual differences. A small but influential line of research has even identified a group sometimes labelled supertaskers.
In a dual-task experiment involving simulated driving and mental arithmetic, researchers identified a minority of participants who showed virtually no performance drop when handling two demanding tasks at once. These individuals tended to score higher on measures of working memory capacity and executive control (proxies for higher IQ), suggesting that cognitive resources, more than motivation or confidence, set the ceiling on multitasking ability.
Working memory is analogous to a computers RAM, in that it determines how many pieces of information can be actively held and processed at once. Individuals with greater working-memory capacity possess more cognitive bandwidth to manage competing demands, though the limits remain real for everyone. In line, studies consistently show that people with higher working memory capacity, stronger attentional control, and better fluid intelligence incur smaller task-switching costs. Working memory capacity predicts resistance to distraction, while Unsworth and Engle (2007) linked it to superior performance in complex attention tasks, and executive attention explains substantial variance in multitasking performance.
The role of personality
Unsurprisingly, personality also plays a role: most notably, traits linked to self-regulation and planning, such as conscientiousness, tend to buffer against the negative effects of multitasking, while impulsivity and related tendencies are associated with poorer performance. Broader Big Five traits such as extraversion, neuroticism, and openness show mixed effect, often influencing how people approach multitasking rather than how well they actually perform it. Even training and domain expertise matter. Air-traffic controllers, surgeons, and experienced gamers show reduced switching costs in their domains because practice automates sub-tasks, freeing cognitive bandwidth.
This does not mean that people know how good they actually are at multitasking. As in most domains of competence, the share of people who claim to excel far exceeds the share who truly do. In a classic experiment, researchers found that heavy media multitaskers rated themselves as effective jugglers of attention yet performed worse on tests of working memory and attentional control. The pattern echoes a broader principle from behavioral science, familiar from the DunningKruger literature: when a skill is poorly understood and rarely measured, confidence tends to rise as competence falls. Multi-tasking, like leadership or emotional intelligence, is easy to overestimate because busyness looks like effectiveness, and we remember the rare occasions when juggling worked, not the many when it quietly degraded our thinking.
Taken together, the evidence paints a nuanced picture. The average human is indeed a poor multi-tasker, especially when tasks are novel or cognitively demanding. But some individuals, by virtue of higher executive capacity (raw mental horsepower), disciplined habits, specialized training, and the right personality, are less bad at it. That distinction matters for leadership and talent assessment, because it reminds us that multitasking ability is not a universal virtue or vice. It is a measurable cognitive skill, unevenly distributed across people, and often confused with confidence, busyness, or the social theatre of productivity.
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If you have ever interviewed for a job, there is a non-trivial probability that you have encountered tricky or quirky interview questions. These are questions that are intentionally unexpected, abstract, or only loosely related to the actual requirements of the role. Rather than systematically assessing job-relevant skills, they are designed to surprise candidates, test composure, or signal creativity.
Interviewers often defend these questions as clever ways to evaluate problem-solving ability, cultural fit, or performance under pressure. The evidence tells a different story. Decades of research in industrial-organizational psychology show that unstructured, brainteaser-style interviews have low predictive validity. They generate noise, not insight. At best, they measure how comfortable someone is with improvisation. At worst, they measure how similar the candidate is to the interviewer.
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Cases in point
To illustrate the point, here are some common examples, ordered from least absurd, or at least somewhat defensible, to most absurd:
1. What is your biggest weakness?Nominally job-related, though usually answered strategically rather than honestly. The only rational way to respond is to disguise a strength as a flaw. It is less a test of self-awareness than an audition for plausible humility.
2. Sell me this pen.Some relevance for sales roles, but still an artificial performance detached from real context. Popularized by The Wolf of Wall Street, it reinforces the myth that great sales is about fast talk rather than listening, diagnosing needs, and building trust.
3. Tell me about a time you failed.In principle, a legitimate behavioral question. In practice, often an invitation to narrate a carefully curated setback that highlights resilience, grit, and eventual triumph. It rewards storytelling ability more than learning agility.
4. How many tennis balls can fit inside a Boeing 747?A classic guesstimate puzzle meant to test structured thinking. Geeks may love it, but it predicts little beyond prior exposure to similar puzzles. If you want to measure cognitive ability, there are far more reliable and validated tools.
5. How many windows are there in New York City?Same logic, further removed from any realistic job task. For what its worth, large language models estimate the number in the tens of millions, depending on assumptions. Which illustrates the deeper point: if ChatGPT can answer it in seconds, why are we using it to judge human potential?
6. If you were an animal, which one would you be and why?A thinly veiled personality quiz. It feels like a BuzzFeed throwback disguised as talent assessment. The answer often reveals more about the interviewers projections than the candidates traits.
7. If you could have dinner with any historical figure, who would it be?A pleasant icebreaker masquerading as a values assessment. It doubles as a signaling exercise: how curious, cultured, contrarian, or provocative can you appear in under 30 seconds? Say Nelson Mandela and you signal virtue. Say Steve Jobs and you signal ambition. Say Machiavelli and you signal strategic depth. But say Stalin and suddenly the interview turns into a moral inquiry. Was that intellectual curiosity, dark humor, or deeply questionable judgment? The question reveals less about your leadership potential than about your risk appetite for reputational self-sabotage.
8. If you were a kitchen utensil, which one would you be?At this point, the exercise has drifted into sheer parody shows like The Office come to mind. Spoon suggests reliability. Knife signals edge. Spork implies versatility. The real variable being tested may simply be how badly you want the job, signaled by the fact that you havent just walked out of the room.
The science
So, what does the actual science of interviewing say?
First, there is evidence that some interviewers are not merely misguided, but derive a certain Machiavellian pleasure from putting candidates on the spot. Research on interviewer behavior shows that individuals higher in everyday sadism or dominance are more likely to ask stress-inducing or intentionally uncomfortable questions. In other words, the brainteaser may sometimes be less about assessing you and more about interviewers enjoying the deviant power dynamic.
Second, the predictive validity of unstructured interviews is consistently low. Meta-analyses spanning decades show that traditional, free-flowing interviews correlate only modestly with later job performance. The problem is not conversation per se, but inconsistency. Different candidates get different questions. Interviewers rely on intuition. Evaluation criteria shift midstream. The result is noise, bias, and overconfidence, and unfortunately, these issues often go undetected because of the subsequent confirmation bias or failure to admit mistakes by hiring managers. In essence, if an interviewer likes you, they will either continue to like you after you are hired or pretend you are doing a great job to avoid looking like a fool.
By contrast, structured interviews work. The formula is hardly mysterious: define the competencies that matter for the job; ask all candidates the same job-relevant questions; anchor evaluations to predefined scoring rubrics; and combine interview data with other validated predictors such as cognitive ability or work samples. Behavioral questions about past actions and situational questions tied to realistic job scenarios consistently outperform seemingly clever riddles and quirky brain teasers.
The role of AI
And then there is AI, not so much the elephant in the room as the bull in the china shop, already rearranging the furniture while we are still debating the seating plan.
In a world where candidates can rehearse flawless answers with generative tools, the theatrical interview becomes even more obsolete. Chatbots can generate polished responses to biggest weakness or sell me this pen in seconds. Ironically, the more predictable and formulaic the question, the easier it is to game. This raises the bar for employers: assessment must shift toward observable skills, simulations, job trials, and multi-source data.
This does not mean interviews become irrelevant. It means they must evolve. When information is abundant and answers are cheap, the premium shifts from rehearsed narratives to demonstrated capability. Instead of asking candidates what they would do, employers can observe what they actually do: solve a real problem, analyze a live case, critique a flawed strategy, or collaborate with a future teammate. AI can help candidates prepare, but it cannot fully fake sustained performance in a realistic simulation.
There is also a deeper irony. The very tools that allow candidates to polish their answers can help employers design better assessments. AI can assist in standardizing questions, generating competency-based scenarios, flagging bias in evaluation, and even predicting which interview questions correlate with outcomes. In other words, AI exposes the weakness of theatrical interviewing while simultaneously offering the tools to fix it. The real risk is not that candidates use AI. It is that employers fail to upgrade their methods accordingly.
In sum, the future of interviewing is not about trickier questions. It is about better design. The uncomfortable truth is that quirky interview questions persist because they are fun, easy, and ego-affirming. But hiring is too important to be left to entertainment. If organizations are serious about talent, they must replace improvisational theatre with evidence-based assessments, and have the humility and self-critical honesty to truly test the outcome of their decisions to acknowledge when they are wrong, and make an effort to tweak things and improve.
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The workplace presents a distinctive set of disclosure dilemmas, beginning with the strange fan dance of interviewing. We are trying to put our best foot forward; to convince our potential employer were a perfect fit and consummate professional, yet were asked, What are your weaknesses? and What are the biggest mistakes youve made? Even the seemingly laidback So, tell me about yourself can feel like a trap. Where should we start?
There has been a lot of buzz in recent years about the benefits of bringing your whole self to work. Theres some evidence for those benefits. Letting others see more of you than you might ordinarily show them forges bonds, including in the workplace. We saw this in the early pandemic, when hardened leaders suddenly turned into endearing softies the moment their toddlers mischievously ran into their home offices.
But for compartmentalizers who prefer to keep work and personal life separate, the bring your whole self to work movement can be something of a nightmare. For others, like me, its freeing. But this new terrain is filled with land mines, and it can be hard to know when youre going to step on one.
The question of how much of our authentic selves to share at work is a pivotal one. Its also a difficult one to answer. We want to share enough to feel understood and connected to others, but not so much that we alienate people or cause them to question our competence or our seriousness.
Making matters even more complicated, each workplace has its own culture and its own norms about the degree of self-disclosure thats deemed appropriate. That doesnt mean theyre clearly articulated, usually far from it. We must discover them. And by no means should everyone decide to simply conform to those norms; bucking them might be good not only for ones own happiness and engagement at work, but for the whole team and for society at large.
So how do we find the right balance? What are the trade-offs between being a little more open at work and keeping strict professional boundaries intact? How much backstage access can we give to our colleagues and our bosses without risking our workplace image?
Backstage versus Front Stage: transparency versus vulnerability
According to my colleague Monique Burns Thompson, who works closely with members of Gen Z, Todays generation craves a level of openness that is different from when I was a young professional. New York University organizational scientist Julianna Pillemers research suggests that revealing aspects of our backstage selves at work, when done thoughtfully, can help us build rapport and stand out in a good way. In workplace contexts, she recommends what Id call discerning authenticitya balancing act that involves giving colleagues some, but not total, access to our inner lives. When done well, Pillemer argues, it helps build trust and sparks more meaningful conversations. Over time, this kind of thoughtful openness can deepen workplace relationships, enhance collaboration, and even improve performance.
What does it mean to be discerningly authenticto be open in a thoughtful way? Pillemer specifies two types of backstage access. The first, which she calls transparency, involves conveying openness by giving people a window into your thoughts, beliefs, or preferences. For example, you might say, Ive always been more drawn to the creative side of things, even though Im technically in a data-heavy role. This kind of sharing can carry some riskespecially if your perspective is unpopular or unexpectedbut it generally offers only a glimpse beneath the surface.
The second level of access, which Pillemer calls vulnerability, goes deeper and carries more risk. It involves sharing potentially sensitive inner states such as intimate emotions, especially negative oneslike admitting that you feel insecure about public speaking or disclosing a disability that might lead others to underestimate you.
For instance, someone might say, I get nervous presenting in front of senior leadership, even when I know the material cold (revealing a performance-related insecurity), or This kind of ambiguity is tough for me. I like having more structure, and Im trying to get more comfortable with the gray area (revealing a trait that might not align with organizational norms).
One shortcut I find helpful is to think of transparency as cognitive openness and vulnerability as emotional openness. In contexts where impressions really matter, the line between transparency and vulnerability becomes a strategic one. Pillemer doesnt draw a hard line, but she emphasizes that vulnerability is riskierespecially in high stakes, evaluative settings like job interviews, where disclosing insecurities might chip away at perceptions of competence. If in doubt, transparency is the safer bet.
Vulnerability should generally be avoided in those contexts unless, say, its framed as a story of growth or overcoming a challenge (I used to struggle with public speaking, so I joined Toastmasters). Even when youre explicitly invited to share something personallike in the dreaded tell me about a weakness questiontransparency often does the trick. You might offer cognitive openness: I think better in writing than I do speaking off the cuff. You could also frame it as growth: Ive learned to prep more deliberately for meetings so I can articulate my ideas clearly in real time. But if you give me a moment to organize my thoughts, Ill always bring sharper insight.
This kind of thoughtful disclosure lines up with what Pillemer would call transparency: revealing how your mind works in a way thats candid but not risky. Vulnerability, by contrast, might involve admitting that you often doubt your abilities or fear being judgeddisclosures that could raise red flags unless carefully framed. Still, even in high-stakes settings, being a bit more open can help.
From Revealing: The Underrated Power of Oversharing by Leslie John published on February 24, 2026 by Riverhead Books, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright 2026 by Leslie John
Youre interested in AI but youre human: Youve got emails to answer, deadlines to meet, and you dont have 40 hours a week to sift through academic papers on large language models.
You just want to know whats happening, why it matters, and maybe how to use it to get home a little earlier.
In that spirit, here are five AI podcasts to help you get smarter and stay informed without wasting your time.
The AI Daily Brief
For the busy professional who needs the headlines fast, theres The AI Daily Brief. Its usually about 20 minutes, which is perfect for the commute or while youre brewing that second pot of coffee.
Host Nathaniel Whittemore does a great job of cutting through the noise, but he doesnt just read the news. He analyzes what the big moves by OpenAI, Google, and Microsoft actually mean for the rest of us.
AI for Humans
AI for Humans is for the “rest of us” who just want to have a good time learning.
Hosted by Kevin Pereira and Gavin Purcell, this show is exactly what it says on the tin: AI news and tools explained by two guys whove been in the tech and media world forever but dont take themselves too seriously.
They demo new tools, they crack jokes, and they make the whole “impending robot takeover” feel a lot less scary. If you want to keep up with the latest without feeling like youre sitting in a lecture hall, give this one a shot.
Practical AI
If youre looking to actually get stuff done, check out Practical AI.
The name says it all. Hosts Chris Benson and Daniel Whitenack aren’t here to wax poetic about the singularity. Instead, they talk about real-world applications.
They interview people who are actually shipping AI products and solving real problems. Their podcast is accessible enough for enthusiasts but technical enough to be useful if youre trying to implement this tech in your business.
The Artificial Intelligence Show
For marketers and business leaders, The Artificial Intelligence Show is required listening.
Hosts Paul Roetzer and Mike Kaput from the Marketing AI Institute were beating the AI drum long before ChatGPT showed up.
They look at AI through a business lens: How does the latest news change your career? How does it change your company? If youre in marketing or management and youre trying to figure out how to navigate the next five years, youd be crazy not to listen.
Eye On AI
Eye On AI is a podcast for anyone interested in seeing the bigger picture.
Hosted by longtime New York Times correspondent Craig S. Smith, this one slows things down a bit. Its biweekly, and the interviews are deep.
Smith talks to the researchers and people building AI systems to better understand the “why” and the “how.” Its less about the “tool of the week” and more about understanding the fundamental shifts in the technology. Its a great weekend listen when youve got a little more headspace.
American statesman and polymath Ben Franklins legacy includes inspirational quotes on frugality, honesty, and hard work. Hes less frequently thought of as an icon of successful aging. But as doctor and author Ezekiel Emanuel recently pointed out on Big Think, At a time when the average age at death was under 40, he lived to 84, fully mentally competent all the way to the end.
That makes the founding father a worthy source of advice on aging well. Whats the biggest lesson we can learn from him.
Unsurprisingly, given he lived at a time when dentures were made out of wood and surgery was done without anesthesia, Franklin cant teach us anything about the latest aging breakthroughs.
But he can remind us of a fundamental truth thats thoroughly backed up by modern science, but still frequently forgotten: Staying useful is as important to aging well as any fancy new drug, fitness routine, or diet plan.
Ben Franklins secret to healthy aging
Ben Franklin was 70 when he signed the Declaration of Independence, and he churned out inventions into his eighties. (Those include inventing bifocals to solve his own issues with failing eyesight). That might leave you with the impression that he was a work-until-you-drop kind of guy. But Emanuel points out thats not actually how Franklin understood his own life.
Franklin invented retirement for working-class people, Emanuel insists. He made enough as a printer that he could retire at 42, and he said, Im going to live a life of leisure.
That means everything that followed the ending of Franklins career as a printer, including much of his work helping to found the University of Pennsylvania and the United States, were technically retirement hobbies.
His golden years didnt look anything like the golf, pickleball, or Caribbean cruises many of us dream about today. But that, Emanuel stresses, is the central wellness lesson we take from Franklins long and exceptionally productive life.
Leisure, for Franklin, didnt mean going to the Jersey Shore. It meant that he didnt have to worry about business and making money. He could focus on doing good, and for him, doing good was science and social improvement activities, Emanuel says. Not contributing to society is not good for the soul. You have to be useful. You have to try to make the world a better place. Thats key to wellness, too.
What modern psychology says about purpose and aging
About 275 years ago, when Franklin stepped away from his first, moneymaking career, he understood that the key to aging well was to find purposeful ways to use his newfound leisure time. Thats a simple enough insight. But research suggests that even today a great many of us fail to remember it.
Research out of Insead, the European business school, shows that many successful entrepreneurs struggle after exiting their businesses with big paydays.
It is perfectly normal to discover that life post-financial freedom isnt as happy as one might have expected it to be, the researchers noted. The most common reason for these problems is a sense of aimlessness and boredom.
Studies of retired Japanese salarymen and personal commentary from many who have pursued the popular Financial Independence Retire Early (FIRE) movement point in the same direction. Many of us dream of wide open days after leaving the world of work. But when confronted with the reality of long stretches of unstructured time, unless people have many explicit plans to stay useful, they tend to spiral.
And not just emotionally. Neuroscience research has found that a sense of purpose helps delay dementia. Its absence, on the other hand, can speed cognitive decline. Meanwhile, an absolute mountain of studies testified that one of the best ways to look after your own wellness is to find ways to help others.
A Google founder and the Governator agree
It can be tempting to think of retirement in terms of numbers. If you have enough saved, your later years will be comfortable and stress free, and therefore healthy and happy, too. But even billionaires seem to flail in retirement unless they, like Ben Franklin, figure out how to continue to contribute to society.
Sergey Brin is worth a cool $200 billion or so. He unretired and went back to work at Google because, he says, I was just kind of stewing and . . . not being sharp. Bill Gates is another guy with no financial constraints, but he, too, has written about how post-work life presents a lot of time to fill and that people need a reason to get out of bed in the morning.
On the other hand, action star turned Governator Arnold Schwarzenegger credits his peace of mind at the age of 78 to a simple life motto: Stay busy. Be useful. Thats basically Ben Franklins whole approach to aging well boiled down to four snappy words.
Healthy aging wisdom thats stood the test of time
So if youre in the market for some good advice on how to stay mentally and physically health for as long as possible, you could look to wellness influencers and tech bros chasing immortality. But all their dubious routines probably wont buy you nearly as many healthy years as Ben Franklins straightforward 275-year-old wisdom.
If you want to age well, stay useful.
By Jessica Stillman, Contributor, Inc.com
This article originally appeared on Fast Companys sister website, Inc.com.
Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy.
The fleeting nature of the Olympic Winter Games makes them all the more alluring. The scarcity is almost sacred. Competitors work their whole lifetimes for one shot at glory that takes place over a period of just a few weeks.
To celebrate every athletic achievement at the XXV Olympic Winter Games, the closing ceremony will take place Sunday, February 22. Heres everything you need to know including how to tune in.
Where will the Milano Cortina Olympic Closing Ceremony take place?
Just like William Shakespeare intended, its fair in Verona where we lay our scene. The Milano Cortina Closing Ceremony will be held at the Verona Arena, which many historians believe predates the Colosseum.
Unlike the opening ceremony, which took place in multiple venues, this is the sole location. Verona lies about halfway between Milan and Cortina d’Ampezzo, the two cities where the majority of the competitions took place.
What is the theme of the Winter Olympics Closing Ceremony?
The theme of the closing ceremony is Beauty in Action.
While exact details of the two-and-a-half-hour event are always kept under wraps for the element of surprise, it is known that the event will celebrate the host country, Italy.
It will also convey climate changes impact on the games and the future challenges this brings. Elements such as music, dance, film, design, and technology will all be utilized to tell these stories and celebrate the games.
Who is performing at the Winter Olympics Closing Ceremony?
The first performer announced was ballet star Roberto Bolle. He is a principal dancer at La Scala Theatre Ballet and frequently performs as a guest artist around the world.
Joining him is singer-songwriter Achille Lauro. He made a name for himself in the hip-hop world but also excels in other genres of music like pop and rock.
Actress Benedetta Porcaroli will also take part in the closing ceremony. She is best known for her work as Chiara in the Netflix series Baby. Additionally, her film credits include Immaculate, The Leopard, and The Kidnapping of Arabella.
DJ Gabry Ponte is planning on dropping some sick beats. He gained prominence as a member of the group Eiffel 65. He even has his own record label, Dance and Love.
Who is hosting the 2030 Winter Olympics?
Another important part of the closing ceremony is handing over the flag to the next host.
The 2030 Winter Olympics will take place in France. The French Alps are already planning for another spectacular competition that will be here before we know it.
How can I stream or watch the closing ceremony?
The ceremony takes place on Sunday, February 22. If you want to catch the action in real time, turn on NBC or the streaming service Peacock at 2:30 p.m. ET.
If that time doesnt work with your schedule, there will be another chance to see the pageantry during prime-time, beginning at 9 p.m. ET.
You can watch NBC for free if you have an over-the-air antenna or a traditional cable subscription.
Peacock is a paid subscription service, but if it’s not part of your streaming arsenal, you can turn to a live-TV streaming service that carries NBC.
YouTube TV, Hulu + Live TV, or fuboTV carry NBC in most areas. Just make sure to double check before you sign up to account for regional differences.
AI is transforming how teams work. But its not just the tools that matter. Its what happens to thinking when those tools do the heavy lifting, and whether managers notice before the gap widens.
Across industries, theres a common pattern. AI-supported work looks polished. The reports are clean. The analyses are structured. But when someone asks the team to defend a decision, not summarize one, the room goes quiet. The output is there, but the reasoning isnt owned.
For David, the COO of a midsize financial services firm, the problem surfaced during quarterly planning. Multiple teams presented the same compelling statistic about regulatory timelines, one that turned out to be wrong. It had come from an AI-generated summary that blended outdated guidance with a recent policy draft. No one had checked it. No one had questioned it. It simply sounded right.
We werent lazy, David told us. We just didnt have a process that asked us to look twice.
Through our work advising teams navigating AI adoption, Jenny as an executive coach, learning and development designer, and Noam as an AI strategist, we have seen a clear distinction: there are teams where AI flattens performance, and teams where it deepens it. The difference isnt whether AI is allowed. Its whether judgment is designed back into the work.
In good news, teams can adopt practices to shift from producing answers to owning decisions. This new way of thinking doesnt slow things down. It moves performance to where it actually mattersand protects the judgment that no machine can replace in the process.
1. The Fact Audit: Question AIs Output
AI produces fluent language. Thats exactly what makes it dangerous. When output sounds authoritative, people stop checking it. It’s a pattern often called workslop: AI-generated output that looks polished but lacks the substance to hold up under scrutiny. In contrast, critical thinking strengthens when teams learn to treat AI as unverified input, not a final source.
David didnt punish the teams that got the statistic wrong. He redesigned the process. Before any strategic analysis could move forward, teams had to run a fact audit: identify AI-generated claims and validate each one against primary sources like regulatory filings, official announcements, or verified reports. The mandate wasnt about catching mistakes, but building a reflex.
Over six months, the quality of planning inputs improved significantly. Teams started flagging uncertainty on their own, before anyone asked.
The World Economic Forums 2025 Future of Jobs Report reinforces this: in high-stakes decisions, AI should augment, not replace, human judgment. Embedding that principle into daily work isnt optional. Its a competitive advantage.
Pro tip: Start with three. Dont overhaul the whole process at once. Ask each team member to flag three AI-generated claims in their next deliverable and trace each one to a source. Keep it lightweight; the habit matters more than the volume.
2. The Fit Audit: Demand Context-Specific Thinking
AI defaults to best practices. Thats by design. But generic advice rarely wins in a specific situation. The real test of critical thinking isnt whether an answer sounds smart, but whether it fits.
Rachel, a managing partner at a global consulting firm, noticed it immediately. Her teams were leaning on AI to draft client recommendations, and the output was consistently competent, but painfully interchangeable. Improve stakeholder communication. Build organizational resilience, she told us. It could have been written for anyone. It was written for no one.
She introduced a simple checkpoint. Before any recommendation could move forward, the team had to answer one question in writing: Why does this solution work here, and not at our last three clients? They had to map every suggestion explicitly to the clients constraints, the firms methodology, and the real stakeholder landscape.
The shift was immediate. Teams started discarding generic AI language and replacing it with reasoning that was theirs. Client presentations became sharper. Debates replaced consensus.
Gallups 2025 workplace data supports why this matters at scale. While nearly a quarter of employees now use AI weekly to consolidate information and generate ideas, effective use requires strategic integration, not just access. Managers are the ones who set that standard.
Pro tip: Make it verbal. While written fit audits are good, ask a team member to explain their recommendation aloud, in a five-minute stand-up or a quick team check-in. Misalignment disappears fast when people cannot hide behind polished text.
3. The Asset Audit: Make Human Contributions Visible
Heres what most managers miss: even when employees are thinking critically, that thinking is invisible. If its not surfaced, it doesnt get recognized, and it doesnt get developed.
Marcus, a VP of strategy at a technology company, started requiring a short decision log alongside every quarterly business review. Not a summary of what AI produced. A record of what the team decided to do with it.
The questions were simple: What assumptions did you challenge? What did you revise? What did you reject, and why? One regional manager used it to flag something the AI had missed entirely: the tension between short-term revenue targets and long-term customer retention. She rewrote the analysis framework to surface that trade-off. The review became a strategic conversation instead of a status update.
It changed what we looked for, Marcus said. We stopped evaluating the output. We started evaluating the judgment.
McKinseys research confirms the stakes: heavy users of AI report needing higher-level cognitive and decision-making skills more than technical ones. As AI handles routine work, the human contribution becomes the entire competitive edge. Making it visible isnt just good management. Its a strategy.
Pro tip: Keep the log short, at just three to five bullet points. What was the AI input? What did the team change? What was the final call and why? The goal isnt documentation for its own sake: its making thinking something the team can see, discuss, and learn from.
4. The Prompt Audit: Capture How the Team Thinks
Critical thinking deepens when people can trace their own reasoning: not just the final output, but the process that shaped it. Without it, every deliverable starts from scrach. With it, the team builds institutional knowledge.
Sarah, a partner at a professional services firm, started requiring a brief process outline before every client presentation. Not a recap of the finished product. A trail: which prompts were used, which sources were checked, where the framing shifted, and why.
After each presentation, team members wrote a short individual reflection: Where did my thinking change during this process? Over time, the artifacts became a shared learning resource. Teams could see which prompts produced shallow output, which revisions added real value, and how collaboration shaped the final judgment.
It turned experimentation into something reusable, Sarah told us. Before, every project felt like starting over. Now, we build on what we have already figured out.
The result wasnt just better deliverables. It was a team that got sharper and faster together.
Pro tip: Create a shared tracker. Keep it simple: a shared doc, a Notion page, or even a Slack channel. Log what prompt was used, what worked, what didnt, and what you would try next. No slides, no pressure. The goal is to normalize small bets and shared learning in real time.
Thinking Critically with AI
AI is only as powerful as the people who use it with intention. The best teams arent winning because they have the fastest tools. They are winning because they have built habits that keep judgment in the loop.
They question what sounds right. They demand context over consensus. They make their thinking visible, and they learn from it.
Managing critical thinking in the AI era doesnt require banning tools or lowering standards. It requires clarity about where thinking lives.
Drawing that line, between what AI should handle and what must stay human, is one of the defining responsibilities of leadership right now. AI changes how work gets done. Management shapes how people think while doing it.
Corporate leaders today are stuck between a rock and a hard place. Nobody can see events playing out in the streets in Minnesota and elsewhere and not be moved in some way. At the same time, they have a fiduciary responsibility to act in the best interests of their stakeholders, regardless of their personal feelings.
I know this dilemma because I experienced it myself. In 2004, I was managing Ukraines leading news organization during the Orange Revolution, the third in a series of nonviolent uprisings known as the color revolutions that overwhelmed autocrats in Serbia and then the Georgian Republic before arriving in Kyiv.
As I explained in my book, Cascades, these things follow a specific pattern of contagion, adoption, and defection driven by networks. Eventually, the nonlinear nature of network cascades overwhelms regimes and compels institutions to act. Now, that pattern is unfolding right here and, for corporate leaders, it is no longer something you can afford to ignore.
1. Contagion: How Movements Learn, Adapt, and Spread
2004 was an election year in Ukraine, so politics was in the air. We all saw the campaigns get underway, with ads hitting the air and rallies being held. But from my vantage point inside a news operation, I also began to hear about a youth group, called Pora, that was organizing students and activists against the regime.
But the true origins started even earlier, in a Belgrade café in 1998. It was there that a small group of five activists met and established the youth group Otpor. Their efforts got a boost from a little-known academic named Gene Sharp, who had developed nonviolent methods of overthrowing authoritarian regimes and established the Albert Einstein Institution to support activists around the world.
The Otpor activists would lead the overthrow of Serbian strongman Slobodan Milošević. Shortly after, West Wing star Martin Sheen would narrate a hit documentary about the events, and activists from other Eastern European countries began reaching out to learn how the Serbians applied Sharps methods. In 2003, President Eduard Shevardnadze was brought down in Georgias Rose Revolution. In the spring of 2004, the Ukrainian Pora activists traveled to Serbia to receive training to lay the ground for the events I witnessed in the Orange Revolution.
We can see a similar process unfolding in Minnesota and beyond. When federal agents began to descend on the community, activist networks first established in the aftermath of the killing of George Floyd were activated. They began to organize to protect their communities from ICE and CBP patrols, learning and honing their methods as they went.
Now, as other communities begin to prepare for ICE and CBP activity, activists around the country are watching and learning. Ordinary Americans are attending trainingonline and in personthat transmits what has been learned in Minnesota: how to organize, dispatch activists, and engage with federal officers on the ground.
2. Adoption: When Participation Becomes the Default
We are a product of our environments. Decades of studies indicate that we tend to conform to the opinions and behaviors of those around us, and this effect extends out to three degrees of relationships. So not only do our friends friends influence us deeply, but their friends toopeople who we dont even knowaffect what we think and do.
Yet the inverse is also true. The people around us are usually doing pretty ordinary things, like going to work, taking the kids to soccer practice, and cooking dinner. Most people who are not actively opposing agents of the state have little idea how to go about doing so. We are, for the most part, trapped in mundane, ordinary lives and resist changing our habits significantly, yet that can change quickly.
In a highly influential 1978 paper about resistance thresholds, sociologist Mark Granovetter showed how even small clusters of individuals, with low barriers to adoption, can influence those with greater resistance. Once these come on board, they begin to influence others as well. It is a pattern we see over and over again: small groups, loosely connected, but united by a shared purpose are what drive transformational change through network cascades.
We can see those same patterns unfolding in America today. Ordinary people, appalled by the actions of ICE and CBP patrols, have joined activists in opposing the raids. As they do, they tell their friends and neighbors, some of whom begin to join in. As they do, their actions influence others who are slightly more reticent and, as they join, momentum builds even more.
I experienced this directly during the Orange Revolution. In the spring of 2004, I was aware of the demonstrations, but not participating. As a foreigner, I wasnt sure it was my place. But then my wifes friends started going and invited my wife. Once she joined in, I began going too and others came with me. The numbers became overwhelming and the regime fell.
3. Defection: When Silence Stops Being Safe
At this point, many readers will begin to notice a problem. Didnt other movements, such as #Occupy and Black Lives Matter, follow these very same patterns and fail to achieve their objectives? The answer, of course, is an unqualified yes. The presence of a network cascade is necessary, but not sufficient, to bring change about. For that, you ned institutions.
Martin Luther King Jr. didnt just organize marches and boycotts. He used the power of mobilization to influence politicians like Lyndon Johnson. In much the same way, in Poland the Solidarity activists didnt just organize strikes. They actively engaged the Catholic Church. Early on during the color revolutions, activists learned that international institutions could be powerful allies and were able to successfully leverage that support.
This is, perhaps, the most striking vulnerability for the present administration. Early on, it targeted institutions, such as law firms and universities, but went about it in a very ham-handed way, and key targets successfully fought back. Others, such as Senators Thom Tillis and Bill Cassidy, have voiced opposition to ICE and CBP tactics. Chris Madel, a Republican candidate for Minnesota governor, ended his campaign in protest.
Yet corporate leaders, despite widely reported misgivings, have been largely sitting it out, even as former CEOs like Reid Hoffman, Bill George and Robert Rubin have urged them to weigh in. Good corporate stewardship, however, requires more than just operating a business and managing a balance sheet. It requires being effective leaders of your corporate community.
Getting Ahead Of What Comes Next
I remember attending a group dinner in Kyiv in late 2007 and sitting across from an executive from Sony Ericsson, who confidently told me that the iPhone launch earlier that year hadnt yet affected his companys sales. Yet the same pattern of contagion, adoption and defection would soon kick in and Sony Ericsson would lose relevance and ultimately be absorbed, as the smartphone cascade reshaped the entire industry.
Once a cascade begins, it takes on a life of its own.
Corporate leaders in America today face a similar dilemma. Their first responsibility is to their stakeholders, whatever their own personal feelings. Yet among those millions taking to the streets are employees, customers, shareholders and their family members. Hoping you can stay on the fence is dangerously naive. It is only a matter of time before someone in your corporate community is affected by ICE and CBP violence: an arrest, getting roughed up, pepper-sprayedor worse.
The time to act is now. If Renee Good or Alex Pretti were one of your people or their children, what would you want to have in place for them and their families? What legal, medical, or psychological support are they and their coworkers going to need? You need to start preparing for that eventuality now.
In much the same way, you need to begin to audit your partners and suppliers. Make sure the people you do business with share your values and those of your stakeholders. If they are supporting or engaging in activities that could harm your corporate community, dont wait for an incident. Cut ties.
Most of all, you need to be explicit about your values and make sure you are living up to them. That doesnt mean taking a political position, but it does mean being clear where you stand. As someone who has had to rise to the challenge of running a business during a revolution, I can tell you from experience that someday you will want to look back on these times, reflect on what you said and did, and be proud of what you did.
Public debate about artificial intelligence in higher education has largely orbited a familiar worry: cheating. Will students use chatbots to write essays? Can instructors tell? Should universities ban the tech? Embrace it?
These concerns are understandable. But focusing so much on cheating misses the larger transformation already underway, one that extends far beyond student misconduct and even the classroom.
Universities are adopting AI across many areas of institutional life. Some uses are largely invisible, like systems that help allocate resources, flag at-risk students, optimize course scheduling, or automate routine administrative decisions. Other uses are more noticeable. Students use AI tools to summarize and study, instructors use them to build assignments and syllabuses, and researchers use them to write code, scan literature, and compress hours of tedious work into minutes.
People may use AI to cheat or skip out on work assignments. But the many uses of AI in higher education, and the changes they portend, beg a much deeper question: As machines become more capable of doing the labor of research and learning, what happens to higher education? What purpose does the university serve?
Over the past eight years, weve been studying the moral implications of pervasive engagement with AI as part of a joint research project between the Applied Ethics Center at UMass Boston and the Institute for Ethics and Emerging Technologies. In a recent white paper, we argue that as AI systems become more autonomous, the ethical stakes of AI use in higher ed rise, as do its potential consequences.
As these technologies become better at producing knowledge workdesigning classes, writing papers, suggesting experiments, and summarizing difficult textsthey dont just make universities more productive. They risk hollowing out the ecosystem of learning and mentorship upon which these institutions are built, and on which they depend.
Nonautonomous AI
Consider three kinds of AI systems and their respective impacts on university life:
AI-powered software is already being used throughout higher education in admissions review, purchasing, academic advising, and institutional risk assessment. These are considered nonautonomous systems because they automate tasks, but a person is in the loop and using these systems as tools.
These technologies can pose a risk to students privacy and data security. They also can be biased. And they often lack sufficient transparency to determine the sources of these problems. Who has access to student data? How are risk scores generated? How do we prevent systems from reproducing inequities or treating certain students as problems to be managed?
These questions are serious, but they are not conceptually new, at least within the field of computer science. Universities typically have compliance offices, institutional review boards, and governance mechanisms that are designed to help address or mitigate these risks, even if they sometimes fall short of these objectives.
Hybrid AI
Hybrid systems encompass a range of tools, including AI-assisted tutoring chatbots, personalized feedback tools, and automated writing support. They often rely on generative AI technologies, especially large language models. While human users set the overall goals, the intermediate steps the system takes to meet them are often not specified.
Hybrid systems are increasingly shaping day-to-day academic work. Students use them as writing companions, tutors, brainstorming partners, and on-demand explainers. Faculty use them to generate rubrics, draft lectures, and design syllabuses. Researchers use them to summarize papers, comment on drafts, design experiments, and generate code.
This is where the cheating conversation belongs. With students and faculty alike increasingly leaning on technology for help, it is reasonable to wonder what kinds of learning might get lost along the way. But hybrid systems also raise more complex ethical questions.
One has to do with transparency. AI chatbots offer natural-language interfaces that make it hard to tell when youre interacting with a human and when youre interacting with an automated agent. That can be alienating and distracting for those who interact with them. A student reviewing material for a test should be able to tell if they are talking with their teaching assistant or with a robot. A student reading feedback on a term paper needs to know whether it was written by their instructor. Anything less than complete transparency in such cases will be alienating to everyone involved and will shift the focus of academic interactions from learning to the means or the technology of learning. University of Pittsburgh researchers have shown that these dynamics bring forth feelings of uncertainty, anxiety, and distrust for students. These are problematic outcomes.
A second ethical question relates to accountability and intellectual credit. If an instructor uses AI to draft an assignment and a student uses AI to draft a response, who is doing the evaluating, and what exactly is being evaluated? If feedback is partly machine-generated, who is responsible when it misleads, discourages, or embeds hidden assumptions? And when AI contributes substantially to research synthesis or writing, universities will need clearer norms around authorship and responsibilitynot only for students, but also for faculty.
Finally, there is the critical question of cognitive offloading. AI can reduce drudgery, and thats not inherently bad. But it can also shift users away from the parts of learning that build competence, such as generating ideas, struggling through confusion, revising a clumsy draft, and learning to spot ones own mistakes.
Autonomous agents
The most consequential changes may come with systems that look less like assistants and more like agents. While truly autonomous technologies remain aspirational, the dream of a researcher in a boxan agentic AI system that can performstudies on its ownis becoming increasingly realistic.
Agentic tools are anticipated to free up time for work that focuses on more human capacities like empathy and problem-solving. In teaching, this may mean that faculty may still teach in the headline sense, but more of the day-to-day labor of instruction can be handed off to systems optimized for efficiency and scale. Similarly, in research, the trajectory points toward systems that can increasingly automate the research cycle. In some domains, that already looks like robotic laboratories that run continuously, automate large portions of experimentation, and even select new tests based on prior results.
At first glance, this may sound like a welcome boost to productivity. But universities are not information factories; they are systems of practice. They rely on a pipeline of graduate students and early-career academics who learn to teach and research by participating in that same work. If autonomous agents absorb more of the routine responsibilities that historically served as on-ramps into academic life, the university may keep producing courses and publications while quietly thinning the opportunity structures that sustain expertise over time.
The same dynamic applies to undergraduates, albeit in a different register. When AI systems can supply explanations, drafts, solutions, and study plans on demand, the temptation is to offload the most challenging parts of learning. To the industry that is pushing AI into universities, it may seem as if this type of work is inefficient and that students will be better off letting a machine handle it. But it is the very nature of that struggle that builds durable understanding. Cognitive psychology has shown that students grow intellectually through doing the work of drafting, revising, failing, trying again, grappling with confusion, and revising weak arguments. This is the work of learning how to learn.
Taken together, these developments suggest that the greatest risk posed by automation in higher education is not simply the replacement of particular tasks by machines, but the erosion of the broader ecosystem of practice that has long sustained teaching, research, and learning.
An uncomfortable inflection point
So what purpose do universities serve in a world in which knowledge work is increasingly automated?
One possible answer treats the university primarily as an engine for producing credentials and knowledge. There, the core question is output: Are students graduating with degrees? Are papers and discoveries being generated? If autonomous systems can deliver those outputs more efficiently, then the institution has every reason to adopt them.
But another answer treats the university as something more than an output machine, acknowledging that the value of higher education lies partly in the ecosystem itself. This model assigns intrinsic value to the pipeline of opportunities through which novices become experts, the mentorship structures through which judgment and responsibility are cultivated, and the educational design that encourages productive struggle rather than optimizing it away. Here, what matters is not only whether knowledge and degrees are produced, but how they are produced and what kinds of people, capacities, and communities are formed in the process. In this version, the university is meant to serve as no less than an ecosystem that reliably forms human expertise and judgment.
In a world where knowledge work itself is increasingly automated, we think universities must ask what higher education owes its students, its early-career scholars, and the society it serves. The answers will determine not only how AI is adopted, but also what the modern university becomes.
Nir Eisikovits is a professor of philosophy and the director of the Applied Ethics Center at UMass Boston.
Jacob Burley is a junior research fellow at the Applied Ethics Center at UMass Boston.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
