Every major technological shift arrives with bold promises of efficiency and productivity. The current wave of artificial intelligence is no different. The forecasts are breathless: tasks automated, workloads reduced, insights unlocked, entire sectors transformed. But behind the promises sits a neglected question: what will work actually feel like?

Efficiency projections tell us nothing about the emotional reality of daily working life. And those emotional realities determine whether people collaborate, innovate, stay in their roles, or quietly disengage.

In an era dominated by AI hype, we need a different lens to understand the future of work. That lens is happiness — not as a perk or a soft ideal, but as a dynamic, measurable signal of how well work is working. AI will transform what we do at work. But only human leadership will determine how it feels to do it. Tracking happiness is the compass leaders need to guide that transition.

The hype is clear: The emotional reality is not

The dominant AI narrative is astonishingly one-dimensional. It focuses on speed, output, and efficiency. Organizations want to know what can be automated, streamlined, or redesigned. But the human experience of work doesn’t hinge on efficiency. It hinges on connection, fairness, autonomy, growth, and meaning. These emotional forces determine whether technology is experienced as liberating or oppressive.

History shows that technological revolutions rarely reduce the pace of work. Email sped up expectations. Smartphones dissolved boundaries between home and office. Collaboration tools multiplied communication channels. In theory, each innovation made things easier; in practice, work often became more intense.

AI could repeat this pattern — or radically improve it. The difference won’t be in the code. It will be in the culture into which AI is introduced. And that is why happiness matters.

Fear is legitimate — and dangerous if ignored

Many workers worry that AI threatens their jobs. These fears aren’t irrational. People sense the scale of change coming, and they know decisions are being made behind closed doors. Fear itself isn’t the problem. But fear left to its own devices is dangerous.

When people are frightened about the future of their roles, their nervous systems switch into threat mode. Threat mode triggers withdrawal: silence, disengagement, reluctance to take risks. That is the opposite of what organizations need during transformation. Creativity shrinks. Collaboration becomes cautious. Initiative declines.

The real danger isn’t AI — it’s secrecy. When AI is developed “behind the curtain” and then imposed on the workforce, people imagine the worst. And in the absence of clear information, imagination rarely paints a hopeful picture. People don’t need perfect reassurance. They need honesty. They need to feel part of the process, not passive recipients of decisions made elsewhere. When leaders treat AI as a social transition as much as a technical one, fear becomes something to work with rather than something that corrodes morale.

Fair and transparent processes create psychological safety — and without psychological safety, no amount of technology will produce good work.

Why happiness is the missing guide to AI adoption

Well-being has been a major organizational focus for years. It tells us whether people are coping; but it rarely tells us whether they are thriving. Happiness is different because when feel people good they do good work, they thrive.

More importantly, happiness is dynamic. It fluctuates week by week, responding to workload, relationships, fairness, and progress. These fluctuations are signals, not noise. They give leaders clear, real-time insight into whether teams feel energized or overwhelmed, hopeful or anxious, supported or alone. This is exactly the kind of frontline feedback leaders need during rapid technological change.

AI will reshape roles and expectations faster than organizations can plan for. Long-term roadmaps will quickly become outdated. Leaders need something more agile — a live emotional dashboard showing how people are experiencing the transition as it unfolds.

Tracking happiness gives leaders that insight. It shows whether AI is reducing friction or creating new frustrations; whether it is freeing time or intensifying workloads; whether people feel empowered or marginalized. Understanding happiness this way it becomes less of a destination and more of a compass — guiding the next step, not the entire journey.

AI can intensify work or liberate it: Leaders choose which

AI itself is neutral. It can speed up work or create space, remove bottlenecks or create new ones, empower people or make them feel monitored. The outcome depends entirely on how leaders implement it. Used well, AI eliminates repetitive tasks, reduces friction, and frees people to focus on creativity, collaboration, and meaning. It opens space for reflection and helps teams spend more time doing what humans do best.

Used poorly, AI becomes a mechanism for intensification. If organizations treat it purely as a tool for extracting more output from fewer people, work becomes more frantic. Expectations rise. Boundaries blur. People feel they are under surveillance rather than supported, and the emotional climate deteriorates.

The crucial question is not what AI can do, but how leaders choose to introduce it. No strategy will succeed if it ignores the emotional experience of work — because a strained emotional climate quietly resists change, while a healthy one helps it take root.

Measure–Meet–Repeat: Happiness as organizational agility

We are entering a future too unpredictable for rigid plans. AI is evolving faster than any previous workplace technology. No leader can reliably predict what their organization will look like in five years. Traditional change-management models — plan, design, cascade — are simply too slow. What leaders need is not an algorithm, but a heuristic: a way of taking the next right step under uncertainty. This is where happiness becomes a mechanism for agility.

A simple weekly rhythm — measure how people feel, meet to discuss what’s working or isn’t, repeat the cycle — creates an adaptive culture. Leaders stay attuned to emotional reality. Teams have a voice. Problems surface early, before they escalate. Small adjustments compound into resilience.

In many ways, Measure–Meet–Repeat mirrors agile methodology. It replaces rigid multi-year plans with iterative adaptation. It helps organizations learn, flex, and evolve as AI reshapes workflows and expectations. Happiness becomes the emotional equivalent of a quick sprint review: a fast, honest pulse of what’s happening on the ground.

In a world where the future is uncertain and the pace is accelerating, happiness is not a luxury. It is a navigation tool.

Why happiness may need to move from “guide” to “core principle”

Most organizations should see happiness as an essential guide during AI adoption. But the more forward-thinking ones will place happiness closer to the centre — because the rewards are even greater. Companies that embed happiness into their AI strategy will not only create better cultures; they will attract better talent. AI specialists, creative thinkers, and the next generation of workers are unlikely to accept burnout cultures or 60-hour weeks. They want flexibility, trust, meaning, and balance. They want to contribute value without sacrificing their health or identity.

In other words, they want good work. Organizations that use AI to create better work — not just more efficient work — will thrive both culturally and technologically. They will retain talent, generate innovation, and future-proof themselves against the rising expectations of Gen Z and beyond. When happiness becomes part of how work is designed, not just how it is measured, the benefits compound: stronger teams, greater creativity, and more sustainable high performance. Happiness is not a distraction from high performance. It is the engine of it.

A brief note on inequality

There is a wider concern: AI may increase inequality. Because AI requires significant capital investment, the gains may disproportionately flow to those who already hold power and resources. This could deepen economic divides and strain social cohesion.

Fairness is foundational to happiness. At work and across society, inequality erodes trust and security. If organizations want AI to create sustainable value, they must consider not only efficiency but also equity. This is not a political argument — it is an emotional and organizational truth.

The future of work may be artificial. But the quality of work will be profoundly human

AI is coming. It will transform roles, reshape industries, and redefine the boundaries of work. But it will not decide whether work becomes better or worse. That responsibility lies with leaders — human leaders — who must understand not just tasks and processes, but people and emotions.

Tracking happiness offers the clearest window into how people are experiencing change. It shows whether fear is being managed or magnified. It reveals whether AI is empowering people or overwhelming them. It helps leaders see whether work is becoming more fulfilling or more frantic. AI will shape the future of work. But only human emotional intelligence will determine whether that future is good. And happiness is the compass that can guide us there.

This article Measure–Meet–Repeat: Why tracking happiness is crucial to AI at work is featured on Big Think.

One of my growing concerns about artificial intelligence is that it increasingly abstracts away the need for mentorship inside organizations. When young people get hired today, it’s becoming easier for managers to spend less time teaching and more time just handing over tools. In the short run, that can look like efficiency. But I do worry about what gets lost over the long run — especially for people just starting their careers.

That’s part of what made this wide-ranging conversation between author and interviewer William Green and Nima Shayegh so enjoyable. I was especially struck by Nima’s reflections on his years of training under Lou Simpson — one of the most respected long-term investors of the past half-century. Like all great mentors, Simpson taught through osmosis: long conversations, careful questions, shared reading, and repetition over many years.

Listening to Nima — now running his own investment firm — talk about that experience was a good reminder that mastery is rarely a solo act. Even with better tools, it still comes from having someone walk alongside you, over time.

Key quote: “There were no Bloomberg terminals. There was no financial TV on. It was like the library of a scholar — a comfortable chair, a couple piles of reading material, and this just very calm presence. He looked at me as he led me inside and he said, ‘Make yourself at home. Let me make you a coffee.’ … And I remember thinking to myself, ‘I should be making you the coffee.’ And when he returned, he didn’t launch into some kind of monologue. He didn’t try to assert how smart he was. He would ask these questions with this very sincere curiosity. And it was this remarkable receptiveness for someone with his experience and with his reputation.”

The all-important context we’re missing

I enjoyed Morgan Housel’s recent essay, “A Few Things I’m Pretty Sure About.” As usual, it’s wide-ranging and reflective.

But what stayed with me most was a simple idea: how much context we’re missing when we judge people — and the world — in real time.

Housel opens with a reflection on chronic pain, then widens the lens. Most harm, he argues, is unintentional. Behavior almost always makes sense once you understand the system surrounding it; the problem is that we rarely have that information while we’re living inside the moment. That perspective carries through his thoughts on housing, AI, and politics.

“When times are good, people get complacent and stop caring about good governance,” he writes. “When times are bad they get fed up and say, ‘Enough of this.’ And I think we’re not far from that today.”

Key quote: “I have a theory about nostalgia: It happens because the best survival strategy in an uncertain world is to overworry. When you look back, you forget about all the things you worried about that never came true. So life appears better in the past because in hindsight there wasn’t as much to worry about as you were actually worrying about at the time.

A few more links I enjoyed:

What the Hell is Water? – Christopher Tsai

Key quote: “‍The idea that much of what we experience in life goes undetected connects deeply to an issue that I’ve been pondering a lot lately: investors’ rigidity — or myopia — in judging companies’ valuations and assessing business durability. In the context of investing, the water in which we swim includes all of the ingrained assumptions, mental shortcuts, and dogmas that many investors accept, often without questioning… This danger is particularly evident when considering the origins of enduring business success, an area where unexamined assumptions tend to blind investors to the subtle forces at play.”

The most successful information technology in history is the one we barely notice – via Big Think

Key quote: “It may seem obvious that books are a technology, yet we rarely think about how they work. They just do. Read most any list of history’s important inventions, and you’re unlikely to find the humble book. The printing press will absolutely be present, possibly taking the top spot. You may even spy paper in the running. While such innovations helped create the modern book — and we’ll discuss them soon — they are also distinct from the book as a technology in and of itself. So, if books were so transformative, why don’t we think about them the same way we do microchips, microwaves, and the steam engine?”

This article How to be a great mentor in business and life is featured on Big Think.

In 1964, spirited debates erupted at a conference in Cardiff, Wales. The source of the consternation was extreme production delays that stemmed from malfunctioning equipment at iron and steel plants across the United Kingdom. Beginning in the twentieth century, the automation of heavy machinery enabled plants to operate continuously, increasing productivity and revenue. The downside was that any small hiccup was acutely felt, cascading through the production line. At first, it was assumed that inadequate lubrication of factory equipment was causing parts to seize up or break apart. And so, the Lubrication and Wear Group of the Institution of Mechanical Engineers, along with the Iron and Steel Institute, called on engineers and representatives from industry to convene and get to the bottom of the prob­lem. But as participants examined photos of equipment failures at plants across the United Kingdom, United States, and Germany, they realized that the prob­lem was not in the lubrication but in the design of the equipment itself. The old designs weren’t equipped to handle the wear and tear of continuous operation, no matter how much lubrication was applied.

In attendance at the conference was Peter Jost, an engineer from the British iron, steel, and tinplate production plant Richard Thomas and Baldwins. Con­ve­niently, one of Jost’s acquaintances, Bertram Vivian Bowden, Baron Bowden, had recently become the UK’s Minister of State for Education and Science. Jost relayed the findings to Lord Bowden and suggested that the newly established Ministry of Technology look into the matter further. When Lord Bowden’s inquiries to the Ministry of Technology went unanswered, he de­cided to take matters into his own hands. He assembled a team of engineers to investigate the scope of the failures and to make recommendations for addressing them. Jost would lead the team.

As the engineers collected data from dozens of plants in the UK, Europe, and the US, a pattern emerged. During operation, as surfaces rubbed against each other, an inadequately designed system experienced excessive wear and tear as well as energy losses. In the UK, these inefficiencies amounted to over £500 million in lost revenue, approximately 1.3–1.6 ­percent of the gross domestic product (GDP). Designing a properly lubricated system that could overcome such inefficiencies, they argued, required an interdisciplinary approach informed by mechanical engineering, physics, and chemistry. 

By late 1965, Jost’s team was ready to pre­sent its findings to the Department of Education and Science. There was just one prob­lem. The title of the report, “Lubrication,” didn’t convey the interdisciplinary nature of the investigation they had undertaken. The marriage of chemistry, physics, and engineering to study how surfaces interact was novel and significant enough to be considered a new discipline of science, and it needed a name. As Jost noted, the team’s description of this discipline, “Interacting surfaces in relative motion and associated practices,” was a mouthful. Hence, some rebranding was in order. 

Jost reached out to the editor of the Oxford English Dictionary, Robert Burchfield, who pointed out that rubbing was the commonality among all the failures. He suggested using the Greek word tribo, meaning “to rub,” as the root of the name. After all, one of Jost’s consultants, David Tabor, had already co-­founded the Laboratory of Tribophysics in Melbourne in the late 1940s. And the Dutch scientist Petrus van Musschenbroek had used the term tribometer to describe an instrument he in­ven­ted to measure resistance in bearings. The editor proposed “triboscience and tribotechnology,” or, if Jost preferred something shorter, “tribology.” At first, not every­ one was on board. But eventually they agreed that a word rooted in ancient Greek would be more easily adopted by an international and interdisciplinary field. Many scientific terms have Greek roots, thanks to the prolific discoveries of the Ancient Greeks. 

In November 1965, the report, titled Lubrication (Tribology), was sent to the Minister of State for Education and Science, a position now held by Edward Redhead. The response was swift and comprehensive. Within the Ministry of Technology, a Committee of Tribology was formed, once again headed by Jost. On this committee, government employees joined academics and industry experts to advise the government on how to implement the findings of the report, including how to provide training in tribology. Soon, centers for the study of tribology emerged across the UK, establishing deep expertise in the science of surfaces and placing it at the forefront of engineering research. Today, Jost is widely known as the ­father of modern tribology. We have him to thank for labs and doctoral degrees devoted to wear, lubrication, and the force that opposes motion. 

Or, as we know it, friction.

This article How “tribology” became a new industrial science is featured on Big Think.

Since the end of the Second World War, technology and management have evolved together like twin helices. Each new machine has required a new way of organizing people around it. The mainframe gave us bureaucracy; the microchip, the matrix; the network, the project team. Every leap in computation produced a corresponding leap in coordination.

Peter Drucker saw this symbiosis first. He realized that the new industrial order would depend on a worker who produced ideas instead of widgets. The knowledge worker became the engine of prosperity, and management became the social technology that synchronized millions of minds. The modern firm was as much an invention as the transistor it depended on.

Three decades later, Tom Peters caught the next wave. As computers left the lab and landed on every desk, he sensed that management itself would decentralize. In The Brand Called You and The Wow Project, he replaced hierarchy with hustle, control with creation. Peters saw organizations dissolving into networks of projects — each one a burst of excellence, a “wow.”

Both thinkers captured their moments, each explaining how machines reshaped management and management reshaped us. Now AI marks the third great inflection point. But this time the pattern breaks. Agentic AI doesn’t just assist managers — it begins to replace them. It can coordinate schedules, allocate resources, even generate strategies faster than any human committee. The connective tissue of the modern firm is being rewritten in code.

What remains distinctly human is not management, but imagination. The leadership that endures is adaptive and creative — the kind that thrives in ambiguity. A relief team improvising during a disaster, a founder balancing innovation with ethics, a general navigating conflicting missions: These are not algorithmic problems but human dilemmas.

That’s why the real revolution isn’t AI itself but the space it exposes: a world where meaning, not management, becomes the organizing principle. From that opening, new forms of collaboration are emerging — “federations of meaning” — loose constellations of people aligned by coherence rather than hierarchy. They mark the next turn in our long dance between technology and humanity — and the first hints of a journey from “wow”to “why.”

The search for why

As algorithms assume the routines of management, humans are left with the one domain machines cannot master: making meaning in uncertainty.

For generations, we organized our working lives around machines — and the systems that served them. When the machines got smarter, the systems grew more intricate. Now AI manages the complexity for us: it tracks the metrics, automates the workflows, predicts what comes next. Coordination has been solved. But direction — the why — is still unsolved.

That gap is where our value has moved. The future belongs to those who can navigate paradox, improvise under pressure, and create coherence from confusion. Think of a doctor weighing empathy against efficiency; a climate scientist balancing realism with hope; a commander deciding whether to trust data or instinct in a fog of conflict. These are not technical puzzles. They are human paradoxes that require discernment, empathy, and imagination.

Yet our institutions were not built for paradox. They were built for predictability. As automation strips away the old hierarchies, people are no longer asking: “What’s my role?” They’re asking: “What’s my purpose here at all?” The era of “wow” — the impressive project, the viral product, the personal brand — has matured into a quieter question: Why? What is all this brilliance in service of?

Out of that exhaustion, something new is taking shape: federations of meaning. These aren’t corporations or campaigns. They are living networks forming around shared intent — scientists and designers reimagining public health systems; technologists building ethical AI collectives; artists collaborating with ecologists to restore vanishing habitats. Their members don’t share a boss or a building — they share a belief that their work must matter.

• A software engineer joins an open-source team designing wildfire sensors.
• A group of teachers co-create a new civic curriculum online.
• A coalition of veterans launches a mentorship network for mental health support.

These aren’t résumés — they’re constellations of meaning. Work becomes less about compliance and more about coherence.

In a world increasingly managed by machines, we are rediscovering what only humans can do: connect, interpret, and create. Innovation without interpretation is empty; progress only matters when it leads somewhere worth going. The next phase of this technological evolution is not about replacing intelligence but reclaiming intention. The question isn’t whether AI will replace us. It’s whether we will remember why we began creating in the first place.

The future of “federations of meaning”

If management was the operating system of the industrial age, meaning is becoming the architecture of the AI age. What’s emerging now isn’t a new company structure — it’s a new human system of collaboration: federations of meaning.

They function less like pyramids and more like constellations — clusters of talent that organize around questions that matter rather than goals that merely measure. Their cohesion doesn’t depend on authority; it depends on alignment. Each participant contributes not because they’re instructed to, but because their values and imagination are activated by the same gravitational pull.

Each technological era has demanded its own kind of leader. The industrial age prized discipline. The information age prized intelligence. The AI age will prize imagination — the ability to make sense of chaos, to synthesize what cannot be reduced to code. Because when decisions can be automated, only direction remains. And direction depends on meaning.

In these federations, leadership no longer flows from position but from presence — the credibility to convene people who don’t have to follow you. It’s the kind of leadership that thrives where logic fails:

• A humanitarian balancing urgency and dignity.
• A CEO reconciling speed with sustainability.
• A general adapting plans amid unpredictable realities.

These are not management problems; they are paradoxes that demand creativity and conscience. Forward-looking organizations are already evolving from fortresses to platforms — from entities that guard resources to ones that gather talent. Their advantage no longer comes from scale but from synthesis: how effectively they host collaboration across difference. The new metric isn’t efficiency — it’s emergence.

And for individuals, success will no longer mean climbing the hierarchy but finding an orbit — connecting with the federations where your “why” aligns with others. In an age where machines can replicate skill, the only true advantage is significance. Meaning is the last monopoly.

Peter Drucker once said that every few centuries society rearranges itself and the world becomes “new.” Tom Peters showed us that we could each author our own work. Now, as AI completes the long arc between technology and management, a third rearrangement has begun — one where intelligence is abundant but meaning is scarce. We are moving from knowledge to meaning, from wow to why.

AI may manage the systems. But only humans can make sense of the story. That’s not the end of leadership. It’s the beginning of what leadership was always meant to be.

This article Why the real revolution isn’t AI — it’s meaning is featured on Big Think.

Nvidia CEO Jensen Huang issued a warning to the world’s workers in May 2025: “You’re not going to lose your job to an AI, you’re going to lose it to someone who uses AI.”

I bet he’s right. The future of work is not man versus machine, but (in part) man with machine versus man without. Augmentation will define that gap: machines not replacing us, but reshaping how we work, what we do, and what it feels like.

Augmentation doesn’t eliminate jobs; it changes them. AI automates what can be codified and frees humans to do what only humans can. The effect is less like replacement and more like amplification, as though we’ve all been paired with the smartest teammate we’ve ever had—the one who never tires, never forgets, and always makes everyone else better.

The evidence is everywhere. Office software drafts emails, cleans up prose, and summarizes meetings. Lawyers use AI to generate first-pass contracts or scan thousands of precedents in seconds, freeing time for argument and client counsel. Doctors rely on systems that triage scans or draft reports while they focus on patients. Farmers fly drones and use sensors that diagnose crops in real time, turning hours of inspection into minutes of targeted care. Coaches analyze athletes’ movements in slow motion with AI, offering feedback at a level that once required a team of specialists. Full automation in these fields is prohibitively complex. But augmentation gives people sharper tools for judgment, creativity, and presence.

This isn’t the first time tools have shifted the boundary between human effort and machine support. The calculator made higher-order math accessible at scale. The spreadsheet transformed finance forever. Google rewired how knowledge workers solve problems. Each collapsed the cost of certain tasks to nearly zero, while elevating the value of human oversight and interpretation. Mathematicians, accountants, and research librarians still had vital roles to play. AI fits that lineage—the difference is speed and scope. It is leaping from calculating to reasoning, from narrow tools to general-purpose collaboration in years, not decades.

The economic stakes are staggering. Globally, OECD estimates that, today, 27% of jobs in developed economies have “high potential” for augmentation, with healthcare, education, and logistics leading the list. Accenture calculates that more than $10 trillion in additional economic value could be unlocked by 2038 through scaled, responsible adoption of generative AI, much of it driven by augmentation rather than replacement. This feels conservative.

It will not always feel liberating. For many, augmentation will feel like acceleration: an endless pressure to do more, faster. It will blur the line between work and non-work. It will raise expectations. In the wrong hands, it could become a tool for surveillance and micromanagement. The challenge for organizations is not just adopting AI but implementing it in ways that build trust rather than corrode it.

To do this, organizations must focus as much on culture as technology. 95% of employees see value in working with AI, but far fewer believe their leaders will implement it in ways that ensure positive outcomes for everyone. The most important choice will be how AI is integrated. Companies that treat augmentation only as a path to efficiency will alienate workers. The ones that thrive will be those that use it to elevate human contribution, redesign workflows, and give people the freedom to focus on what only they can do. That requires more than investment in technology—it requires leadership that prizes adaptability, experimentation, and shared benefit. Technology won’t define the future of work. Leadership will.

For individuals, I have some counterintuitive advice: “upskilling” is overrated. Success won’t depend on learning the mechanics of AI. The tools will be self-evident, they’ll change constantly, and they’ll change too rapidly to make technical mastery a differentiator. The people who will thrive will be those willing to adapt their practices, experiment with new ways of working, and abandon habits that no longer serve them.

I call this behavioral adaptability: the capacity to alter one’s practices and habits in response to changing conditions. Unlike technical skills, which expire, behavioral adaptability endures because it reflects a disposition toward flexibility, experimentation, and unlearning.

In the next decade, behavioral adaptability will be the highest-value quality in the workforce. The individuals and companies that cultivate it will not only keep pace with change, they’ll set the pace of change. Even with strong credentials or resources, those who resist will be left behind. In the age of AI, behavioral adaptability will become the true currency of work.

New work

Automation is eliminating jobs. Augmentation is reshaping today’s jobs. But every major technological leap has created forms of labor that would have been unrecognizable beforehand. AI will be no different. New work is what comes next.

Some of that new work will actually look like a return to old work. As machines absorb the transactional and computational, demand will rise for what only humans can provide: qualities like presence, care, artistry, and experience. Coaching, teaching, caregiving, counseling, and artistic performance will grow in value. So will the work of craftspeople, physical therapists, and chefs. In an age where abundance makes everything synthetic, a hand-made meal or a live performance will carry a premium. There will be cultural hunger for authenticity, and these roles will not be marginal. They will be central. AI will make things cheap and it will make people precious.

Our jobs will also head outward, toward the frontiers of science and exploration. AI will accelerate breakthroughs in climate adaptation and geoengineering, synthetic biology, quantum and fusion research, immersive education, even interplanetary logistics. It will expand the human project into domains once reserved for science fiction, and create new ones.

The reality is though, much of the new work ahead will be impossible to imagine until it arrives. It’s easy enough to point out that in an AI economy, the value of human-centric work will rise, but it’s impossible to tell you what jobs that don’t yet exist, will. It’s an unsatisfying answer, people hate when I give it to them, but it’s the one history has proven true over and over again. A century ago, no one could have predicted jobs like cybersecurity analyst or cloud architect, let alone flight attendant. Yet today, digital work defines vast portions of the global economy. The same will happen again. Entire categories of work will appear that we cannot yet name but that will feel indispensable to the next generation.

Work has always been more than economic output. It is the scaffolding of culture and identity. As the definition of valuable work changes, so will our sense of status, belonging, and a life well lived. AI will not end work. It will expand it, pushing us toward new ways of creating, connecting, and adding value that are invisible to us today.

This article How “new work” will actually take shape in the age of AI is featured on Big Think.

In 1895, the world’s top scientist predicted wrong. Epically wrong.

The scientist was Lord Kelvin. Born in early nineteenth-century Ireland to a mathematics teacher, he was from his youngest summers a wizard with numbers. Like a living computer, he thought effortlessly in digits, entering college at the age of ten and dashing up the ranks to full professor. By his middle age, he had reduced electricity to algorithms, unified the known rules of physics, and formulated thermodynamics, achieving such eminence that in 1892, he became the first scientist elevated to England’s House of Lords.

It was three years later, while serving out his final term as president of the British Royal Society, that Kelvin made his epically wrong prediction: “I can state flatly that heavier than air flying machines are impossible.” In other words: There will never be an airplane.

Years passed, the century turned, technology advanced. Across the Atlantic at the North Carolina beach town of Kill Devil Hills, a pair of thirty-something brothers, Orville and Wilbur Wright, began crafting a flyer with an aluminum engine, wing warps, and a movable rudder. Kelvin, meanwhile, remained implacable. In 1902, he declared to a reporter from The New York Journal: “No balloon and no aeroplane will ever be practically successful.”

One year later, the Wright brothers flew.

In our modern space age, it’s easy to laugh at Lord Kelvin. He was a luddite, a fogey, a stick in the mud. But we shouldn’t be so quick to smirk. The mistake that the good lord made then is one that almost everyone on earth is making now.

The mistake is to confuse probability with possibility. Probability is the statistical likelihood that an event will occur — and so too, in modern parlance, is possibility. All that distinguishes the two terms nowadays is that possibility is on the lower end of the probability spectrum. If you ask a data scientist to define a possibility, they’ll reply: It’s a small probability — say ten percent or less.

This is wrong. Probability and possibility occur in different regions of the human brain. They do so because their underlying physical mechanics are fundamentally distinct.

Probability is computed from an analysis of prior events. Those events are correlated with one another, yielding patterns that in turn produce statistical forecasts. By definition, a probability is therefore an event that has happened before — usually, multiple times.

A possibility, in contrast, is an event that has never happened before. What makes it possible is that it doesn’t violate the laws of its environment, so it could occur, if initiated. As an example, there’s American football. American football has rules that dictate how points are scored, when the clock runs, and what players aren’t allowed to do. But those rules don’t mandate innovations like the forward pass, the spread offense, or the 4-3 defense. From the sport’s beginning, such innovations existed as possibilities — permitted but not preordained by the rulebook — as do other sporting inventions that have yet to (and may never) be introduced to the game.

If you can’t imagine those inventions, your brain is stuck in probability. Probability is how computers think. And it’s how Kelvin thought too. He was certain that machine flight was impossible because it had never happened previously. Its odds were therefore zero.

Possibility, meanwhile, is how children think, because children think in story. Story makes possible the worlds of science fiction, which (like American football) are based on rules that circumscribe action without determining it, encouraging our brain to speculate: Maybe in that future world, I could do X; maybe with this tomorrow tech, I could do Y.

The Wright brothers were emboldened to think in story by their father, who permitted them to skip school so long as they spent the day reading authors like Mark Twain. And by thinking in story, the Wright brothers imagined what Lord Kelvin could not: A contraption that did something radically new.

Today, most of us think like Kelvin. We’ve been conditioned by the rise of quantitative economics, mathematical forecasting, Bayesian statistics, “moneyball,” AI, organizational psychology, system 1 and 2, quantum mechanics, management science, MBA programs, and the rest of our algorithmic age to think that life can be reduced to computer spreadsheets. In those spreadsheets, we see patterns — aka, probabilities. By reasoning this way, we believe we’re seeing the future. Yet really, we’re shutting our eyes to potential innovations, limiting our personal growth and missing the first hints of big change.

It’s possible, however, to restore our vision. All we need to do is train up the storythinking brain regions that the Wright brothers activated with their father’s bookshelf. For a comprehensive guide on how to do so, see my book Primal Intelligence, but as a quick start, here’s a simple two-step exercise.

First, to get your team thinking in terms of possibilities, distribute a list of extraordinary economic events. Ask your team members to circle events that have happened before and to cross out events that could never happen. Then explain: The circled events are probabilities and the crossed-out events are impossibilities. Which means that all the remaining events — uncircled and uncrossed-out — are possibilities.

Second, present your team with a lightly sci-fi scenario in which a rule of their current operating environment is altered by a near-future technology (e.g., stem-cell therapies that reverse aging, or highways that wirelessly charge electric vehicles while driving). Having disrupted your team’s priors, ask them: What potentially profitable initiatives could we launch that don’t contradict the new rule? 

Give each individual team member five minutes to propose possible responses that can’t be validated with data but also can’t be shown to be impossible. Award one point for every response, and allow participants to “steal” a point if they can show that another participant’s response is either a probability or an impossibility.

In trials at Fortune 50s and SMEs, teams who practiced this two-step exercise improved significantly at proposing initiatives that were both novel and feasible. And the more you practice the two steps yourself, the more your own brain will do like the brothers Wright: Exit probability for possibility, where imagination can take flight.

This article How to be as innovative as the Wright brothers — no computers required is featured on Big Think.

In his sweeping 2025 year-in-review, the author Dan Wang makes an unsettling argument: The real danger of AI isn’t psychotic robots taking over the world, but how it’s compressing our sense of time to think in ever-shorter durations.

As Dan writes, in Silicon Valley today, conversations are increasingly collapsing into apocalyptic timelines. Leaders fixate on what to do over the next year, while often neglecting the harder work of extending timelines. Dan’s concern isn’t only that leaders think short-term, but that thinking around AI has become either utopian or apocalyptic, which makes long-term institution-building feel irrelevant.

In contrast, Dan argues China treats AI less as a civilizational endpoint and more as an input — something to be embedded into factories and industrial capacity over decades, rather than a single technology that determines history all at once.

“I am not a skeptic of AI,” Dan writes. “I am a skeptic only of the decisive strategic advantage, which treats awakening the superintelligence as the final goal. Rather than ‘winning the AI race,’ I prefer to say that the US and China need to ‘win the AI future.'”

Key quote: “I’m bothered by how quickly the discussions of AI become utopian or apocalyptic. As Sam Altman once said (and again this is fairly humorous): ‘AI will be either the best or the worst thing ever.’ It’s a Pascal’s Wager, in which we’re sure that the values are infinite, but we don’t know in which direction. It also forces thinking to be obsessively short term. People start losing interest in problems of the next five or ten years, because superintelligence will have already changed everything. The big political and technological questions we need to discuss are only those that matter to the speed of AI development. Furthermore, we must sprint towards a post-superintelligence world even though we have no real idea what it will bring.”

Why everyone seems to be gambling now

A recent essay circulating on Twitter has a provocative premise: The reason why people are gambling more than ever — via sports betting, prediction markets, or stocks — is the result of perfectly rational behavior in a world that’s becoming increasingly irrational.

The author’s core claim is simple: the traditional bargain no longer works. Work hard, stay loyal, save patiently — and you’ll be rewarded — used to be a viable path. Today, wages lag assets and housing is out of reach.

When patience no longer pays, people stop being patient. What fills the void is agency. Casinos — broadly defined — offer something the modern economy increasingly withholds: the feeling that individual judgment might matter. Even if the odds are bad, gambling offers conviction, immediacy, and the possibility (however slim) of escape.

Key quote: “Every month brings new benchmarks showing AI matching or exceeding human performance on tasks that used to require expensive degrees and years of training. White-collar workers, or otherwise the financially aspirational, are watching the timeline shrink. Three years ago, ‘AI will replace knowledge workers’ was a thought experiment. Now it’s a planning assumption. Everyone’s asking when, not if, and the estimates keep getting shorter.”

A few more links I enjoyed:

The Era of the Business Idiot – via Ed Zitron

Key quote: “‍We live in the era of the symbolic executive, when ‘being good at stuff’ matters far less than the appearance of doing stuff, where “what’s useful” is dictated not by outputs or metrics that one can measure but rather the vibes passed between managers and executives that have worked their entire careers to escape the world of work. Our economy is run by people that don’t participate in it and our tech companies are directed by people that don’t experience the problems they allege to solve for their customers, as the modern executive is no longer a person with demands or responsibilities beyond their allegiance to shareholder value.”

The Lost Art of Leading – via The Beautiful Truth

Key quote: “Our culture is obsessed with leadership. From best-selling biographies and podcasts to conferences, coaches and consultants, the quest to unlock the secrets of great leadership is everywhere. The belief that better leadership is the key to individual progress and organizational success has fueled a multibillion-dollar industry. But for all the theory and all the investment, we seem no closer to the kind of leadership we actually need. If anything, the opposite is true.”

This article How AI is making us think short-term is featured on Big Think.

Time blocking is a simple practice: You determine exactly when you will work on your daily tasks, usually at the start of the day, in blocks of time that you then “chunk” your day into. I like to schedule things in thirty-minute blocks and make an effort not to plan to do something for longer than ninety minutes.

Carving out this time away from work to logically think through your day accomplishes a couple things. First, you get to comprehensively consider all the things you will want to accomplish — this lets you make progress on everything you need to, so nothing slips through the cracks. (It helps to keep your list of work goals nearby when you define your time blocks.) Time blocking also, just as important, provides you with greater confidence that whatever you’re working on in the moment is what you should be working on. This means you’ll experience less doubt as you work, because you will have predetermined that whatever it is that you’re spending time on was the best possible thing to be doing. 

To make time blocking as effective as possible, what you block time for must be grounded in a deep knowledge of what’s important. When it is, you ensure your momentary actions contribute to a larger purpose. Each block of time becomes a brushstroke that paints a picture of what you will accomplish. 

Over the last decade of my studying and writing about productivity, time blocking has unfortunately never really stuck for me (until I changed my relationship with the practice). I have always found the technique a bit constraining. As someone whose highest value is self-direction, I like feeling in control and don’t like being told what to do. Maybe, weirdly, this extends to being told what to do . . . by myself. 

In the past, to time block I would typically chunk the following workday out at the end of the current one — this way, I could loosely think about what I would be spending time on between when I finished work and started the new day. Returning to my desk the following morning, though, my reaction was invariably the same: I’d look at my upcoming day with resentment and dread. Even if plenty of breaks were interspersed between my focus blocks and I was working on stuff I found enjoyable, seeing that most hours of my day were predetermined for me instantly made me feel tired. Instead of feeling as though a day was full of opportunity and promise (along with a few meetings), I saw basically every time block as an obligation, determined for me by some previous version of myself. I typically didn’t go so far as to rebel against my past self, but I was tempted to. Screw this guy, I’d think. I’ll spend my time however I want!

Just as the same set of actions can help us attain multiple goals, most of my time-blocked and non-time-blocked days probably looked the same. I had things to do and I got them done. What changed was my relationship with my work. 

When I was in the planning stages for this book, I knew I needed to write about the practice: Time blocking is a remarkable technique for ensuring your daily actions are guided forward by your overarching goals and intentions. But I also wanted to make the technique work for me: I set out on a mission to figure out a way to time block that helped me accomplish what I’d sought to do — while not feeling as though I was losing autonomy in becoming more intentional. (The irony of this is not lost on me.) 

My time blocking breakthrough came in the form of a rather basic (and, in hindsight, obvious) realization: that I didn’t need to schedule my entire day ahead of time, all at once. Time blocking, as it is most commonly practiced, involves scheduling your whole workday as you block off time for different things. To feel more autonomy in the moment, at the start of the day, I still defined how long I’d be working on various tasks. But after defining what I would spend time on, I didn’t schedule anything. Instead, as the day went on, I’d choose which time block to start next. Sometimes, I’d choose the next few things to work on; other times, I’d choose just the next one. It was a simple change, but I still experienced the benefits of having predefined what I would spend time on — with the control I wanted.

It took a bit of extra work during the day to block the hours of my day as it went on, but this let me hold on to the feeling of being in control — even if my days would have looked the same either way. At the start of the day, I made a list of all the blocks I wanted to spend time on (and for how long). Over time, I’d come to think of the ritual as “rolling time blocking.” 

My ritual is an easy one, and I typically like to schedule my day the analog way. In the morning, on the lefthand side of a sheet of paper, I write the hours of the day I’ll be working. Then, as the day begins, I’ll slot in the first few blocks. And go from there. As the day goes on, I now typically have the next few blocks scheduled. This takes advantage of dedicating long blocks of time for focused work but still gives me the freedom to direct my day. Of course, it’s important to make sure you have time free in your calendar to fit those blocks into. If you’ve tried time blocking your day and the ritual hasn’t stuck for you, I highly recommend giving this modification a shot.

This article “Rolling time blocking”: Your next great productivity ritual is featured on Big Think.

In the blink of an eye, artificial intelligence has been set to work transforming every walk of life — from self-driving taxis, to software that reads X-rays as accurately as radiologists, to virtual assistants that can schedule meetings and draft emails, to original if derivative music created in an instant in the style of Mozart or Marley. Like disruptive technologies before it — think automobiles, mechanical textile looms and more — it promises to radically change the world we live in, including the world of work.

Fascinated and alarmed, economists and policymakers are debating how AI — and especially much anticipated artificial general intelligence, or AGI — will reshape the workforce. Techno optimists argue that technology has historically been a powerful driver of economic growth, spurring new industries with novel jobs. That’s what happened with the advent of the automobile, after all: The ranks of carriage makers, horse breeders and stable owners melted away as jobs opened up in the emerging oil industry, and then in brand new sectors like motor hotels — motels — and drive-in theaters. Why couldn’t the same happen with AI?

But others hold that the changes wrought by AI are of a different scale. International Monetary Fund economists have estimated that AI may affect as many as 40 percent of all jobs as AI-driven machines replace work that was traditionally performed by people, much of it skilled. And even where jobs aren’t lost, work by human beings could become less valuable, causing wages to fall, says Anton Korinek, an expert on the economics of AI at the University of Virginia.

With such worries widely felt, it is perhaps no coincidence that two of the three recipients of the 2024 Nobel Prize for economic sciences have written extensively about artificial intelligence and its potential impact on jobs. MIT’s Daron Acemoglu and Simon Johnson argue that we must act deliberately to ensure that AI’s benefits are shared widely — through government intervention, bold new policies and reskilling programs to avoid deepening inequality and societal unrest in this age of growing automation.

The two economists advise that, as we navigate this moment, we heed lessons from the past — specifically, the early Industrial Revolution, another time of economic and social upheaval, and the flexible thinking of a key figure of that time: Englishman David Ricardo.

Acemoglu, Johnson and their colleagues say that if, like Ricardo, policymakers act with care and flexibility, AI might even help restore what the start of the tech boom put in jeopardy: decent-paying middle-class jobs.

Changes hit home

Ricardo, born in 1772, was a parliamentarian and noted economist of his time. In his younger years, he was a techno optimist of sorts. He believed that new spinning machines that converted raw cotton into yarn were going to increase worker productivity and prove to be beneficial for everyone — workers, entrepreneurs and the public.

The new machinery might initially displace some home-based spinners, he recognized, but eventually those people would find work elsewhere.

English parliamentarian and economist David Ricardo did not foresee the drastic effects of industrialization on workers’ livelihoods. “The same cause which may increase the net revenue of the country, may at the same time render the population redundant, and deteriorate the condition of the labourer,” he later wrote. Credit: Public domain / Wikimedia Commons

And that’s what happened — at first. Cotton textile manufacturing boomed during Ricardo’s lifetime: The new spinning machines developed in the 1770s made producing yarn faster and cheaper. Workers who had spun yarn at home on spinning wheels were disrupted by these new machines, but many were able to transition into another growing cottage industry — weaving the now more abundant and cheaper yarn into cloth.

The first edition of Ricardo’s Principles of Political Economy and Taxation, published in 1817, makes no mention of the potential ill effects of machinery on workers. Indeed, in an 1819 speech before the English House of Commons, he declared that “machinery did not lessen the demand for labour.”

But a different reality was emerging with a second invention: power looms, introduced roughly a generation after the spinning machines. A single power loom could produce more cotton than 10 to 20 handweavers working from home, and the machines were so large that they had to be housed in factory buildings, taking cottage industry weaving off the table. As factory weaving eclipsed home weaving, this time the displaced workers had no place to go, because power looms created relatively few new jobs in the factories.

For home-based weavers, this was a disaster. Family earnings for handloom weavers in two Lancashire towns fell by half over a five-year period starting in 1814, Acemoglu and Johnson recount in a 2024 article in the Annual Review of Economics. Handloom workers in the English cotton industry overall averaged 240 pence per week in 1806, but by 1820 — around the time Ricardo was making his speech in the House of Commons — they were making less than 100 pence weekly.

Handloom weaver wages plummeted after the advent of power looms. Wages for factory workers were not high and did not see growth in the early 1800s. (Numbers shown are nominal wages, not adjusted for inflation or changes in the cost of living.)

Even the factory workers who had jobs tending the powerful new textile looms weren’t faring well. They experienced little real wage growth between 1806 and 1835. Growing wealth inequalities spawned social unrest, especially in the hard-hit industrial north. A major demonstration, with an estimated 60,000 people clamoring for political reform, was broken up by deadly force in Manchester in August 1819, in what is known as the Peterloo Massacre.

Ricardo, who had witnessed firsthand the consequences of power looms in the cotton industry, radically changed his mind. A more nuanced view of mechanization found its way into the third edition of Principles, published in 1821. He inserted a whole new chapter to discuss machinery’s impact, writing, in what amounted to a recantation: “The same cause which may increase the net revenue of the country, may at the same time render the population redundant, and deteriorate the condition of the labourer.”

“Middle skill” fortunes fall

Ricardo’s pivot holds important lessons for today’s labor economists and policymakers as they wrestle with the impact of AI’s impact on jobs and wages, Acemoglu and Johnson say.

But this is not a problem that began with artificial intelligence. Rather, it has been brewing since the 1980s with the proliferation of digital technologies that were soon to dominate so much of business, commerce and society — computers, the internet, smartphones, e-commerce, social media. Just as with power looms at the start of the Industrial Revolution, these failed to spread profits to ordinary workers. Instead, Johnson says, what they’ve done “is help the most skilled or the most educated.”

Since the 1980s, meanwhile, the wages of “middle skill” people — such as those who didn’t attend four-year colleges — stagnated or fell in real terms. The tech revolution “automated away a broad middle-skill stratum of jobs in administrative support, clerical and blue-collar production occupations,” writes David Autor, a labor economist at MIT, in an article in the magazine Noēma. Like the spinners and weavers of yore, those people saw their livelihoods melt away. The result was a hollowing out of the middle class, Autor writes, forcing many to turn to less-skilled, lower-paying work.

Indeed, while average household income in the United States increased by 95 percent after adjusting for inflation over the past four decades, according to the Peter G. Peterson Foundation, a nonprofit focused on the United States’ fiscal health, those gains varied widely among top earners and lower-income groups. In the highest income group, income rose 165 percent between 1981 and 2021, whereas among the lowest group, it grew only 38 percent: The wealthiest group did more than four times better than the lowest income group.

Earning disparities are increasing in the United States. (Numbers are in 2021 dollars and don’t factor in the effects of taxes or assistance programs.) Credit: Peter G. Peterson Foundation

How AI can help

And now comes AI. Will it worsen the trend — or, if it’s handled right, might it present an opportunity? Johnson, Autor and Acemoglu express optimism. “AI — used well — can assist with restoring the middle-skill, middle-class heart of the US labor market that has been hollowed out by automation and globalization,” Autor writes. By training individuals in the application of AI software, middle skill workers might be able to take on many decision-making tasks previously assumed by doctors, lawyers, software engineers and even college professors. An experienced medical worker, for example, could master a new medical device, like the use of a new type of catheter, or could carry out an unfamiliar procedure during a medical emergency.

In the health care sector, “there are so many expert decisions to make, so much technology in use, and so many rigorously trained care providers who are not MDs but who could likely perform at a higher level — that is, do higher-stake tasks — with better support tools,” Autor says. Lung ultrasound, for example, is a promising technology for diagnosing patients with shortness of breath. Although it’s typically performed and interpreted by medical doctors, in a recent study health care clinicians not experienced in lung ultrasound “were successful in obtaining high-quality lung ultrasound clips using an AI tool,” Autor says.

Elsewhere, Autor adds, the HVAC industry (heating, ventilation and air conditioning) is drawing on AI tools that provide real-time guidance and remote diagnostics to enable HVAC technicians to solve more complex problems. Technicians who are already on-site in a home or building can use a ChatGPT-type AI tool for troubleshooting problems using a flowchart. Earlier, they might have had to reach an expert by phone for help.

A pre-AI analogy to these kinds of changes would be the rise of nurse practitioners, says Johnson. “There weren’t very many nurse practitioners 30 years ago. And now every pediatrician’s office has them. And parents are very grateful for the advice and access that they can get, because they have these highly skilled people who do not have MD degrees, but they are enabled — they have been empowered by their licenses.”

The idea isn’t to replace doctors, Johnson adds. “We’re just saying: Redress that balance. Boost the people who don’t have four years of college. Enable them to be more productive.”

Not all economists think this sort of solution will work with AI, though, especially once AGI, with human-level cognitive abilities, arrives on the scene. “The key distinction from past technological changes is that AGI would be capable of performing any cognitive task, potentially leaving few unique economic roles for human workers,” Korinek says. “This could lead to widespread labor displacement and significant wage declines” — unless governments intervene to avoid further widening of the wealth gap.

Growing economic hardship of workers in England in the early part of the 1800s led to civil unrest. In 1819, up to 18 people were killed and hundreds injured during protests in the northern city of Manchester when cavalry charged into the crowd. The event, which became known as the Peterloo Massacre, led to increased suppression in the short term but ultimately to reforms such as an expanded right to vote. Credit: Manchester Libraries / Public domain / Wikimedia Commons

In the short term, Korinek says, this might mean upskilling workers in roles requiring still uniquely human abilities like “authentic human connection, emotional intelligence and ethical oversight,” where machines don’t excel or don’t satisfy a human need. Examples could include psychotherapists, childcare providers and religious counselors.

Further down the road, societies might need larger shifts, such as sharing prosperity through the implementation of a universal basic income: no-strings-attached regular cash payments from governments that citizens receive whether or not they work.

Guidance from the past

There are few convincing studies at this point on the impact that AI, including AGI, will have on global jobs and wages. Partly this is because researchers have to take into account jobs that don’t yet exist in industries not yet invented. As we wait for this new world to unfold, however, the life and times of David Ricardo might offer some food for thought.

The new machinery introduced to English textile factories in the early 1800s had a much worse impact on workers’ lives than Ricardo ever anticipated. It took him a long time to realize this. When he did, he was nearly 50 years of age, wealthy, well-connected and at the top of his profession. It would have been easy to ignore the untoward effects from these amazing machines, the power looms. Instead, Ricardo admitted his mistake and modified his theories. That kind of fluid thinking, and intellectual integrity, will be crucial in the age of AI, Johnson says. “We have much to learn from Ricardo’s openness to new ideas and new ways of thinking about economics,” he and Acemoglu write in their paper.

This 1835 image depicts a scene from a weaving mill with women working at power looms. The rise of factory weaving devastated the livelihood of hand weavers. Credit: E. Baines / History of the Cotton Manufacture in Great Britain / Public Domain

England did learn from the handloom weavers’ collapse, and the travails of its laboring class generally. It expanded the political voice of its industrial cities. Before 1832, cities like Manchester weren’t even represented in Parliament. England’s Factory Act, passed in 1833, introduced what was arguably one of the world’s first enforceable child-labor factory regulations. (It required factory inspections.) The government repealed its protectionist Corn Laws in the 1840s, which made food more plentiful and cheaper for the urban laboring class. Ricardo, a free trade advocate, was a powerful voice urging the Corn Laws’ repeal.

The new world is hurtling toward us. Daniel Kokotajlo, former governance researcher at ChatGPT developer OpenAI, who now heads the AI Futures Project, has posited a scenario in which AGI is fueling an economic boom by 2027 — but is also causing millions of job losses because its software is outperforming humans in coding, research and other cognitive tasks. (“It might take a few years longer than 2027,” he says now.)

Other predictions differ — they range from a world in which more and more wealth becomes sequestered with the few, to one in which AI helps to reduce inequalities. It depends a lot on how our governments elect to act. “All the smarts, all the talent around computer science is being drawn into the pursuit of AI, to a degree that I haven’t seen since the internet really boomed at the end of the 1990s,” Johnson says.

But, he adds, as a look at the industrial revolution and history more broadly makes clear, “just because you have new miracle machines does not mean most people will benefit.”

This article What happens to the weavers? Lessons for AI from the Industrial Revolution is featured on Big Think.

With the year winding down and my inbox now filled with out-of-office replies, I thought this week’s Nightcrawler was a good excuse to recommend a couple of feature-length documentaries.

The first is The Thinking Game (free on YouTube) — a five-year portrait of Demis Hassabis, co-founder of DeepMind. On one level, it’s a fascinating account of how a small group of researchers pushed the limits of artificial intelligence and produced genuine breakthroughs.

Beneath the surface, the film is really about long-term thinking. AI can feel like it appeared out of nowhere sometime around 2022. This documentary shows how misleading that impression is. The real story began much earlier — with years of false starts, doubt, and incremental advances that rarely made headlines. When DeepMind’s breakthroughs finally arrive, they are the result of a few individuals who refused to abandon a problem that had defeated scientists for half a century.

Key quote: “My view is that the approach to building technology, which is embodied by move fast and break things, is exactly what we should not be doing. Because you can’t afford to break things and then fix them afterwards.”

Real journalism in an era of fake news and misinformation

The second documentary that I recently enjoyed was Turn Every Page (available on Amazon Prime), a story about the creative relationship between Robert Caro (author of The Power Broker) and his long-time editor, Robert Gottlieb.

Like The Thinking Game, the film operates on multiple levels. On the surface, it chronicles a complex, sometimes fraught, 50-year partnership between two men devoted to telling great works of nonfiction. More fundamentally, it serves as a kind of meditation on the need for deep, investigative journalism in a world where that feels increasingly rare.

Key quote: “People are always asking me why I chose Robert Moses and Lyndon Johnson to write about. Well, I must say I never thought of my books as the stories of Moses or Johnson. I never had the slightest interest in writing the life of a great man. From the very start, I thought of writing biographies as a means of illuminating the times of the men I was writing about and the great forces that molded those times — particularly the force that is political power.”

A few more links I enjoyed:

Is the Tao smarter than the Dow? – via Tom Morgan [Special thanks to Tom for giving me a very kind shoutout here.]

Key quote: “‍Individual investors and traders who consistently believe they’re smarter than the collective human intelligence of the market often end up in deep trouble. Societies that believe the collective human intelligence of the market is smarter than the Tao seem destined to do the same…. More broadly, I’m curious if win-win businesses can survive and thrive in an economy that’s becoming increasingly win-lose. So a core question I wrestle with in my work is: is the Tao smarter than the Dow?”

Sustaining Institutions in a World Focused on Short-term Performance – via Wendell Weeks

Key quote: “Dominic Barton has called for a shift from quarterly capitalism to long-term capitalism. He argues that focusing on quarterly earnings is not the right way to assess a business’s value to shareholders, much less its value to society. I agree, and believe that the focus on quarterly earnings is pressuring executives to focus on incremental product improvements and rapid pay-offs, while under-investing in the big, disruptive innovations that drive growth and increase productivity. Now, Dominic and others have spoken far more eloquently than I can on what this means from a capital investment perspective. So I’ll share my thoughts about what it means from a broader perspective. But let me anticipate some concerns and say what it does not mean.”

This article The inside story of DeepMind is featured on Big Think.

Despite these times of extreme change, uncertainty, and complexity, many leaders still expect that they, and the people who work for them, should leave their worries at the proverbial office door. If that ever was a reasonable expectation, however, it clearly no longer is.  

Across industries and at all levels, people are overwhelmed, exhausted, and burning out like never before. The consequence: ever-growing disengagement, which undermines individual well-being and organizational productivity and performance.

In its most recent State of the Global Workplace report, Gallup found that the percentage of engaged employees dropped from 23% to a meagre 21% last year — a decline equal to that seen during COVID-19 lockdowns. 

There are many contributors to this. Friction around return-to-work orders. Increasing financial stressors and polarization. The rise of AI. 

And there is yet another big one that fails to get the attention or response it deserves. As the authors of another Gallup report, The State of the World’s Emotional Health 2025, succinctly put it, “The world is on an emotional edge.” 

Have you ever spent most of your day worrying? It turns out, 39% of the more than 145,000 people Gallup surveyed said they did — just the day before. In addition, hundreds of millions more people routinely experience worry and stress than a decade ago, the report also finds.

Of course, we live in what experts call a polycrisis: an age of many interconnected crises seemingly happening everywhere at once. These range from the cost-of-living crisis to the climate crisis, the misinformation crisis, and the mental health crisis.

Given all that, the prevalence of worry is surely understandable. It just isn’t healthy or helpful. As abundant research shows, worry tends to narrow thinking, impair decision making, and erode connection — none of which is good for leaders, teams, organizations, or society.

So, what would happen if we flipped the script — from worry to wonder? Or to put another way, to stop feeling that we somehow need to save the world and start focusing more on savoring it? 

From worry to wonder

When we speak of wonder, we often think of what might better be described as moments of awe, such as the joy of seeing thousands of starlings fly in synchronized, swirling movements above our heads. 

But wonder is much more than a momentary feeling we either stumble upon or don’t. When we cultivate wonder as an orientation to leadership and life, it helps us — and others — open up to possibility. It reduces stress, rumination, and emotional exhaustion. It also fosters creativity, connection, and resilience.

As the late Rachel Carson wrote in a little gem of a book called The Sense of Wonder, “Those who contemplate the beauty of the Earth find reserves of strength that will endure as long as life lasts.”

And, yes, I know. That’s all well and good for weekends and holidays. But leaders can’t be expected to take their teams out to contemplate the beauty of the Earth when financial pressures, deadlines, and customer and stockholder expectations are looming. How could this apply in the workplace? This is where savoring comes in. 

Savoring in the workplace

Savoring is the act and art of extending positive feelings — about anything. You don’t have to round up your team and head to the woods. 

For example, one leader recently told me that she takes photos of salamanders, which she finds fascinating, and sends them to a team member whose son loves them. It takes very little time, but it extends her positive feelings and, by sharing them, forges connection. 

Another leader I know makes it a habit of saying something he genuinely appreciates about the people he works with, week after week. Doing so rarely takes more than a minute or two, but the impact is enduring, as people tend to be more open to him than they would be to those who lean more toward criticism or have no sense of appreciation at all. 

So, what if, in these times when so much needs fixing — and most of us feel time-crunched — savoring were a simple and effective way to build a culture where leaders and teams have more emotional bandwidth to face complexity and stay creative without burning out? 

What if, to put it another way, we thought of savoring as a much-needed sense of leadership maturity and helpful antidote to the fantasy that people leave their abundant worries at the office door?  

What the research shows

A growing body of research suggests savoring is about much more than you may suspect — namely, being nice. 

One 2025 study published in the journal Personality and Individual Differences finds that feeling and extending positive emotional states — “a phenomenon known as savoring” — leads to improvements in self-esteem, self-compassion, mindfulness, and positive reappraisal, a coping strategy in which people deliberately reinterpret a stressful or adverse event in a more constructive, meaningful, or beneficial way. 

Research also shows that savoring reduces stress and other forms of psychological distress, as well as depression. And strengthening positive emotions and mental well-being, in turn, tends to improve our capacity for positive interactions with others.

Several things are important here that may not be immediately obvious: One is that savoring is about self-agency. It is about choosing to extend our positive feelings and well-being rather than letting those moments quickly pass away like a leaf blown by the wind. 

This means that instead of experiencing ourselves as people who may or may not stumble upon a moment of awe, wonder, or joy, we become people who are both on the lookout for such experiences — and give them legs. Cultivating savoring in the workplace helps leaders and teams catch, as it were, otherwise passing good moments and helps them become a bigger part of us. It puts the many benefits of awe and other positive experiences more in our control. 

Second, we tend to have habits that either amplify or dampen the experience of savoring. Some of us, for example, have a habit of minimizing the positive emotions associated with positive experiences — quickly moving on to the next thing or adding what didn’t feel right — perhaps because someone else chided us for being too emotional, too Pollyanna-ish, or just plain too much. Others, in contrast, may embrace such experiences as we might a particularly delicious meal. We slow down and give it space to fill us up. 

Finally, the domain of things leaders and teams can savor, as suggested above, is not limited to those occasions of stumbling on a glorious sunset or winning a big new contract. Research shows that, even in hard times, people can reap the benefits of savoring experiences they had in the past or anticipate in the future. 

Savoring, in other words, doesn’t depend on the happenstance of external things. It rests with us to choose it as an orientation to leadership and to cultivate a culture that counterbalances the many stressors in today’s workplaces, both those arising at work and those brought into the office. 

Ultimately, it is developing practices that help people stop getting caught up in so much unproductive, unhealthy, and disempowering time spent in worry and, instead, reconnect with the wonder of what is good as a wiser, healthier, and more effective orientation to our uncertain times.

This article Why the best leaders help their teams to “savor” the world is featured on Big Think.

When Simon Squibb was kicked out of his family home aged 15, he quickly built up the first of many businesses that helped him transform his name into a global brand. A gardening venture born of teenage survival kick-started the journey that would lead to the creation of YouTube’s most-watched business video: “30 years of business knowledge in 2hr 26mins.” (15M views to date)

More than 18 million social media followers now watch Squibb hand cash to strangers in the street if he likes their business idea. He also bought a staircase in London with a doorbell for people to “pitch their dreams,” and now has a similar doorbell in New York, which he runs with Sir Richard Branson.

Here, Squibb reveals to Big Think how he became a “professional talker” and how other leaders can explain ideas more clearly; why he’s opposed to traditional school education; and why he wants to give all his money away rather than leave it to his son.

Big Think: You’ve built 19 businesses, and invested in more than 80. Do you think entrepreneurs are made — or born? 

Simon Squibb: Anyone can start a business. Anyone can make money by doing what they love. My entrepreneurial muscle was activated by circumstance. My father died when I was 15: a sudden heart attack. One second he was there, the next he was gone. Then my mum and I argued — she said, “Get out of my house,” and I took her at her word. I never went back. 

I was homeless for eight weeks; on the streets, friends’ sofas, in a stairwell. Without a National Insurance [UK Government ID card], I couldn’t get a job. I realized my only option was to create my own. I saw a big house with a messy garden, and knocked on the door. I had no equipment and had never done gardening, but quoted them £200 a month [about $400 USD back then] to do it up, and they agreed. I knocked on more doors, found more customers, and convinced the original household to let me borrow their lawnmower and tools. It was a bit awkward, because I’d positioned myself as having a gardening company, but I think they just saw a young kid trying to do something and helped me out.

Big Think: You went from being homeless at 15 to selling marketing business Fluid for multi-millions. What did that journey look like?

Simon Squibb: With more gardening clients, I could rent a room in a house. With an address, I could get a National Insurance number and find a job in customer services, then in a shop.  Looking back, I was just fighting to survive. But weirdly, I now think of it as a positive experience. 

I dabbled with employment for a while. It was quite nice not to have to worry about bringing in a set amount of money, just getting a monthly payment from the boss. But I didn’t last long: my brain was too busy thinking about business ideas. Working for someone else made me weak. I wasn’t creating anything. 

So I started one of my early businesses, Accommodation Express. It was a bit like Hotels.com, before the internet. I built a referral system so when people called a full hotel, my company would help them find another room. I went on to build 19 businesses, but when Fluid was bought by PWC, I turned 40 and retired. I thought it was a dream come true: never having to work again. For six months, it was great. But I was basically a money manager: it was boring. I didn’t have a purpose. I needed to use my brain, and felt quite desperate to find something new to do.

Big Think: Money motivates most of us. Without that requirement, what drove you? 

Simon Squibb: Yeah, money was not my motivation, which was quite nice. I could think: What do I like to do? I remembered how painful it was at 15, trying to build a business without any help, or access to capital. I could see all these people selling courses that most couldn’t afford. So I thought, why don’t I just start sharing the knowledge that I’d got for free? 

That’s what I started doing: going on YouTube and TikTok and sharing business knowledge for free. There are loads of people doing it now, but I was the only one back then who wasn’t selling a course.

Big Think: You’re known for taking a contrarian stance — “You don’t need money”; “Getting ahead of 99.9% of people is easy” etc. What fueled this sideways approach to teaching?

Simon Squibb: It’s a hook model. People think, “You can’t start a business with no money.” Yes, 100%, you can. Most of the best businesses have been started with no money. But with social media, you’ve got to have something that makes people stop and watch. It’s a combination of a hook and helping people think differently as quickly as possible.

A lot of people have beliefs that aren’t true. That’s the thing about the hook — you have to challenge them, fast. People think, for example, that it’s safer to work for someone else. But the real safety is in owning your own destiny. If you’re working for someone else, they might lay off 10,000 people, like Meta just has. 

Because I left school and home at 15, I’ve never been on a normal path. If you ask someone if a 15-year-old should leave school to start a business, of course, they’ll say no. But because I’ve lived an unconventional life, I see that way is often the best. I now know things that you’re told at school aren’t always true.

Big Think: You homeschool your son — why do you think that the education system doesn’t help entrepreneurs?

Simon Squibb: At school, you’re ranked based on exams you have to do on your own. An A is success; a D is failure. But in the real world, I would never do an exam on my own. I’d get help; ten people to do the exam together, and we’d all get an A. I’ve never sat at a desk and built a company on my own: I work with teams. In the real world, teamwork is what leads to success. The ones who try alone often fail. But in school, the focus is all about teaching individual achievement.

Big Think: On social media, your success is rooted in your ability to make complicated things simple. Where did your gift for explaining come from?

Simon Squibb: I practiced. Like anything, you get better with repetition. I made videos every day for six years, which sharpened my content-making. Just because one video works, it doesn’t mean the next will. Two days ago, I posted a video interviewing someone on the street and asking, “What’s your dream?” and it got 30 million views. The next day, a video with the same opening only had 500,000 views. So, you iterate, experiment, and innovate. 

When CEOs or founders need to explain things in an interesting way, sincerity always wins. Openness, honesty, and authenticity. CEOs can’t hide behind a corporate brand anymore: People want to know who’s behind the company, what are they’re going to do with my data, and are they ethical? What are they doing with the profit? I think “for profit, for good” companies are the future. People want to connect to the business purpose and the owner. And if you’re a CEO of a company and you don’t have a social media presence, you’re going to find it harder to get a job.

Big Think: Talking of finding it harder to get a job, graduates are leaving universities and struggling to find work as AI wipes out thousands of roles. What AI businesses most interest you now?

Simon Squibb: Every business has AI in it now. Years ago, people would ask if companies were online or had a website. Now, of course, everyone does. History is repeating itself. AI is becoming prevalent in every business. The mistake people are making is investing in “AI businesses” — it’s companies that have a purpose and that invoke AI that will boom. I’ve invested in Lovable [the Swedish vibe-coding startup]: you can voice clip the app you want it to build, and it will build it for you. Coding and the technical product-build side used to be a major barrier to building a business. It’s not anymore. Now you’re only limited by your imagination. That’s exciting. 

But yes, a lot of the traditional jobs are going to be lost. Graduates are struggling to get jobs. But that means entrepreneurship is about to explode. If people can’t get a job — just like me at 15 — then they will have no choice but to create one.

Big Think: You have a positive mindset. You think education doesn’t work, so teach in your own way. You think AI will kill swathes of jobs, but bring in more entrepreneurial opportunities. Do you have an upbeat mantra by which you live your life?

Simon Squibb: The purpose of life is living with purpose. When I retired at 40, I realized money alone doesn’t provide meaning. Financial advisors flocked to me, talking about putting the money into trusts and avoiding tax. But I look at it like: where can the money go that’s useful? 

I could give it to my son, but my job as a parent is to give him a fishing rod, not give him a fish. If I’m a good parent, he should be able to make his own money. If I just give him a load of money — and I‘ve seen people do that — the outcome is not positive. People lose their drive and lean into drugs, alcohol. It’s not healthy to give any one person — even my son — a load of money. 

So my dream right now is to fix the education system and to fund people’s dreams. I think if I do that, the world will become a better place for my son to live in. That’s a better legacy than a lump of money for the sake of it. And it gives me a purpose every day too.

This article The art of the hook: How Simon Squibb redefines influence is featured on Big Think.

Over the last few years, AI has dazzled us all. It can write our emails, optimize our workflows, and create alarmingly realistic videos. But is it intelligent? Can it actually understand things?

I’m not so sure. This week, I published an essay for my Long Game column in Big Think about this idea. To do that, I tell a story about a slime mold that might just be more “intelligent” than a human — even though it has no brain. (No, I haven’t lost my marbles yet; read it and you’ll understand.)

The basic premise is this: the world is pouring incomprehensible amounts of energy and capital into building systems that mimic a single, narrow form of human intelligence. But in the process, we may be ignoring a far older, and potentially wiser, form of intelligence. It’s also a type of intelligence that is in high demand as the world changes faster than ever.

Key quote: “In our rush to create artificial minds, we seem to have forgotten to study the real ones. Because once you start noticing the intelligence threaded through the natural world, you realize that the planet is already saturated with superintelligent systems. We just don’t recognize them because they don’t use language or wear lab coats or hoodies. To me, water possesses intelligence: the way it adapts to its container, negotiates obstacles, carves canyons given enough time. Trees possess intelligence: the way they share nutrients underground, warn neighbors of threats, modulate growth according to light, shadow, and wind stress. Whole forests operate like decentralized minds.”

In defense of a good night’s sleep

Readers of this newsletter will know that I have — and sometimes complain about — having two young children who keep me up at odd hours of the night.

In modern tech, investing, and business culture, that’s almost a badge of honor. Sleep is treated as a kind of weakness. I’ll sleep when I’m dead, the ambitious founder says, eyes bloodshot, productivity app glowing. Occasionally, the counterargument appears: that sleep is essential for performance and creativity. This, of course, is true. But it still frames sleep as a tool or a means to an end.

But this essay by Sara Protasi, a professor of philosophy at the University of Puget Sound in Washington state, pushes the idea somewhere far more interesting. To her, sleep is one of the few things we do that makes us truly human. It’s an opportunity to turn our conscious minds off, and become more connected to the universe.

Key quote: “In this sense, sleeping is intrinsically valuable because it involves the experience of radical vulnerability. When we sleep, we are first and foremost living beings — we are not, and ought not to be, machines in perpetual motion, constantly recharging and plugged in. We are animals inhabiting a planet in a solar system, with lives shaped by natural rhythms that stem from its particular characteristics: alternating seasons, days and nights.”

A few more links I enjoyed:

Everyone is Gambling and No One is Happy – via Kyla Scanlon

Key quote: “‍I don’t think you can understand the economy until you understand how we talk about the economy. I think there we are dealing with a combination of (1) postpandemic adaptation and (2) the whole smartphone-induced micro-solipsism thing layered onto (3) younger generations watching objective unkindness rewarded by politics. People (understandably) are dealing with epistemic drift too, what some might call “medieval peasant brain” due to the constant influx of Internet (people are putting potatoes in their socks to draw out toxins, for example).”

Stop Reading News – via Farnam Street

Key quote: “When was the last time reading the news made you wiser? Not more informed — wiser. We stuff ourselves with headlines and updates, believing that more information makes us smarter. Yet this daily flood of news does the opposite: the more we take in, the less we actually understand. It’s like trying to drink from a fire hose — we’re drowning in facts but starving for real knowledge. The modern news cycle is ruthless by design. Stories flash bright and die fast, like striking a match. As technology has made news free and instant, we’ve traded careful thought for speed, real insight for endless updates. The result? The few truly important things get lost in an endless stream of chatter.”

This article A new way to think about intelligence is featured on Big Think.

In the race to build superintelligent AI, high-caliber data is everything. Mercor, a data annotation startup, has emerged as one of the fastest-growing companies in history by offering something most rivals don’t: expert-labeled evaluations that benchmark the capabilities of large language models.

AI evaluations (“evals”) are structured assessments that measure how well a model performs on tasks within specific domains. Instead of low-wage workers tagging data, Mercor hires doctors, engineers, lawyers, investment bankers, and other professionals to judge model outputs for quality and accuracy. 

A software engineer, for instance, might evaluate code for security flaws or functional completeness, then design a rubric developers can use to benchmark improvement. Those rubrics become the scoring guides that machine learning engineers deploy to grade model responses, flag failures, and define what a “good” answer looks like. 

Put simply: evaluations form the baseline that AI companies use to steer their models towards outputs they desire.

“We are in the age of AI evaluations,” Mercor co-founder and CEO Brendan Foody said on a September episode of Lenny’s Podcast.

Since launching in January 2023, Mercor has inked deals with frontier model developers like OpenAI and Anthropic, as well as six of the “Magnificent Seven” tech giants, including Alphabet, Amazon, and Google. In just 17 months, Mercor saw its annual revenue run rate surge from $1 million to $500 million. The company claims it now employs more than 30,000 experts, paying contractors an average rate of $95 an hour to test and score model performance. As of October 2025, Mercor’s valuation sits at $10 billion, with investments from Felicis, Benchmark and General Catalyst.

Its meteoric rise is emblematic of a strategy built on the need for high-quality data, confident leadership, and a bet that expert-driven evaluations will define the next phase of the AI arms race. But as companies pour more money into AI training data and see their models evolve, a growing number of analysts and investors are wondering whether Mercor may be flying too close to the sun.

The rise of Mercor

In January 2023, three college dropouts — Foody, Adarsh Hiremath, and Surya Midha — launched Mercor as an AI talent marketplace connecting programmers with U.S. companies. Foody and his co-founders quickly recognized a shift underway in the AI ecosystem. After meetings with OpenAI and xAI, they pivoted the company toward expert-driven evaluations, betting that model developers would increasingly rely on professional judgment to advance their technologies. Human expertise, they believed, would unlock the next wave of AI performance.

As the company built out its evaluation platform, contracts with top AI labs followed. A turning point came when an undisclosed rival tapped Mercor to source more than 1,000 contractors for its own projects. The operation quickly unraveled under a wave of support tickets from unpaid workers. For Foody, it was a wake-up call about the inefficiencies and vulnerabilities of middlemen: a chance to bring more of the process in-house.

“The wealthiest companies in the world are willing to spend whatever it takes to advance model capabilities,” Foody told Lenny’s Podcast.

That willingness to spend has fueled Mercor’s aggressive expansion. Today, the company claims it pays out roughly $1.5 million a day to its evaluators. And with growing demand from AI labs seeking better performance benchmarks, the expectation is that spending will only increase.

But with high pay comes high standards. One annotator, who worked on Mercor’s nutrition and dietetics projects, said the company enforces strict expectations. “[Mercor] has very high standards, and they will offboard people really quickly if they’re not submitting quality work,” the contractor, who asked to remain anonymous, told Big Think.

It’s clear that the need for Mercor’s highly-skilled talent has never been higher. In 2025, that momentum reached a new peak. In February, Mercor closed $100 million in a Series B, bringing its valuation to $2 billion. Then, in mid-June, Meta invested $14.3 billion in Scale AI, triggering a wave of customers to turn to rivals like Mercor. More than six months later, Mercor raised an additional $350 million in Series C led by Felicis, multiplying its valuation five-fold to its current valuation of $10 billion. 

“Brendan has shown remarkable focus and discipline,” Sundeep Peechu, who led Mercor’s last two rounds of investments as Felicis’s managing partner, told Big Think. “He’s a rare mix of visionary and operator.”

Mercor’s AI evaluations, Peechu adds, have become “cornerstone infrastructure” in the AI ecosystem as the economy leans into what Mercor calls a “reinforcement learning (RL) environment machine,” where human feedback continuously shapes the trajectory of AI systems.

But not everyone is convinced the company can keep up its pace. 

The long-term growth question

Tech analysts and investors agree that Mercor’s business will continue to grow in the short-term. But some experts say its long-term growth hinges on how quickly AI capabilities develop. 

John Bersin, founder of The Josh Bersin Company, a market research and advisory firm, questions whether Mercor’s experts are truly top-tier or simply skilled contractors helping models edge forward. He doubts that seasoned professionals have the time or incentive to spend hours evaluating AI outputs. 

“A true expert is likely to be either too busy or too financially successful to spend days annotating data,” Bersin told Big Think. “It’s more than likely that the labeling experts are simply contractors helping the system become a little smarter than before.”

Scott Chou, co-founder and managing partner of the ESO Fund, says the company may potentially run out of qualified talent to hire. “I don’t really get Mercor,” Chou told Big Think. “It seems a lot like Scale, which smartly exited before the bottom came out. There’s value and sales to be had in the near term. But they’re operating in a space where sustaining growth over time will be difficult.”

Edward Hartman, partner at Simon-Kucher, a growth consulting firm, sees risk either way. He says that if AI gets good fast, Mercor could be “undercut by an oversupply of newly displaced professionals.” If it doesn’t, the need for human oversight could disappear altogether. Either way, he said, the infrastructure might outpace the revolution it’s built to support.

In response to these concerns, a Mercor spokesperson told Big Think that there are no signs the talent pool is shrinking and that AI is creating new jobs and skills in the workplace. In other words, human judgment still has a critical role to play in shaping machine intelligence, and there’s a long way to go before AI will reach peak capabilities.

“I know there’s been some executives at big labs that say we’ll have superintelligence in three years,” Foody said on Lenny’s Podcast. “But I think the truth is that it’s a longer road.”

This article Inside the meteoric rise of Mercor is featured on Big Think.

There are some people in life we feel compelled to listen to. There’s something about the way they deliver their message that draws us in. Interestingly, when we look deeper, the detail of what they say seems to be less important than their delivery and their ability to capture an idea in a simple way. 

The world is full of influencers, politicians and celebrities that — when you scratch beneath the surface — have remarkably little to say but nevertheless have scores of people queuing up to hear them say it. Rather than get frustrated by this conundrum, we need to learn from it. In almost every line of work, whether we’re salespeople, managers or leaders, trying to influence our friends, our families or our boss, we want what those lucky few get: cut-through. 

The Cambridge English Dictionary defines cut-through as ‘success in getting people’s attention and influencing them’. The aim of a great pitch or presentation should be nothing more and nothing less.

Of course, you need to strive for more than just soundbites and clickbait. If there’s no substance to your message people will quickly be distracted by the next shiny thing. But cut-through is the Trojan Horse. It gives you a foot in the door and gifts you the possibility of opening your audience to what comes next. 

In their book Smart Brevity, Jim VandeHei, Mike Allen and Rory Schwartz, co- founders of media platform Axios encourage us to “adapt to how people consume content – not how you wish they did or they did once upon a time. Then change how you communicate immediately.” It’s sound advice. 

The world of presentations and pitching has changed little in the last few decades. Yes, we have moved on from overhead projectors, but for the most part, people’s approaches have remained largely unchanged. And that’s not a good thing. 

If we look instead at the world of my former career as a TV and film actor, the contrast is stark. Go back and find a drama made 30 years ago and the majority of them will feel plodding and ponderous. I loved the BBC series All Creatures Great and Small as a child and back then I was completely captivated. Revisiting it now, it appears painfully slow. The FX series The Bear by contrast is like being on a rollercoaster. The choppy editing and searing soundtrack left me feeling, in moments, quite unable to breathe. News available 24 hours, overflowing social media feeds and incessant digital marketing have primed the modern human brain for a different type of engagement.

Rather than resisting, we need to learn to adapt. The first thing to embrace is emotion. 

Emotion is the currency of attention, and it stops distraction in its tracks. If you can make your audience feel something, if you can leave them in a different emotional state at the end of your interaction compared to where they were at the beginning, you have earned the right to be remembered. You have taken the first step towards cutting through the noise. However, emotion alone is not enough. The second element required to create cut-through is simplicity. Complexity is a sure-fire way to lose your audience. Instead, you need to be able to take complicated ideas and deliver them in a way that is easy to understand. 

Without straying into politics, there is a clear trend at present for simple slogans and phrases of as few words as possible. At least initially, audiences don’t seem to have an appetite for detail, they want to connect with the high-level concept before they will entertain the nuance. That means you need to aim for economy. The simpler your message, the more likely it is to land. 

Finally, to ensure that you leave a lasting impression, the third element needed for cut-through is energy. You must be seen to believe in what you’re saying, otherwise you can’t expect others to. Energy in a pitch or presentation context may seem somewhat intangible but as an audience member you know when you’re on the receiving end of it. As a presenter it’s your job to find the right energy to breathe life into your ideas. 

Emotion. Simplicity. Energy. If you want to create cut-through, you need to bring these three elements together in every pitch and presentation you deliver. They are the secret sauce. Their intersection is the sweet spot you should be aiming for.

This article How to harness the Trojan Horse of presenting skills is featured on Big Think.

Back in the 1960s, NASA spent millions of dollars designing a zero-gravity pen. The Russians, on the other hand, used a pencil.

This story — which, I admit, is likely apocryphal — underscores something we see every day across work and life. We pour massive amounts of time and energy into complex solutions for complex problems. Sometimes, the answer is as simple as using a pencil.

Which begs a bigger question: Why are we all so drawn to complexity in the first place? To explore this, I’d recommend a sharp essay by Carl Hendrick titled, “Why Does Thinking Feel So Hard?”

Hendrick explains why “smart,” complicated solutions can feel productive, even when they’re not. Sometimes you really do need to invent a zero-gravity pen for a very challenging issue. Other times, a bit of graphite and wood will do the job just fine.

Key quote: “[Psychologist R.H.] Waters claimed that students instinctively chose the route that demands the least mental work. But strangely, they did this even when they knew a more effortful pathway would lead to deeper understanding. At a time when psychology was dominated by behaviorism, and education was steeped in what would later become the moral language of grit and discipline, this was an uncomfortable proposition. Effort was seen as a virtue, not a variable. Waters’ suggestion that learners actively minimize mental effort, not out of laziness, but as a basic cognitive strategy, was both unconventional and ahead of its time.”

Why co-founder relationships fail

I thoroughly enjoyed this Founders podcast with Michael Ovitz, the legendary founder of Creative Artists Agency (CAA). It’s a roaming conversation about ambition, excellence, curiosity, pattern recognition — and, perhaps most interestingly — why most business relationships fail over time.

To Ovitz, it’s usually not about a divergence of strategy, or even ego. It’s something far simpler: most business partners never develop truly complementary temperaments that enable them to build something for the long-term.

Key quote: “I don’t want to put a percentage on it because I honestly don’t know. But if you asked me off the top of my head, I’d say 90% [of co-founder relationships] fail. It’s very hard to have two strong founders that share a singular vision… It’s very hard to find leaders who understand principles of business, how to execute them, how to handle people, how to be a leader, how to get along with your co-founder, how to have an intellectual process to support your vision, and how to unfold your vision, and while you’re doing that, to be open-minded. That’s really difficult.”

A few more links I enjoyed:

Hidden Reality of Alien Intelligence & Biological Life – via Michael Levin and Lex Fridman

Key quote: “‍When cells disconnect, they physiologically disconnect from the other cells. Their cognitive light cone shrinks. The boundary between self and world, which is what the cognitive light cone defines, shrinks. Now they’re back to an amoeba. As far as they’re concerned, the rest of the body is just external environment, and they do what amoebas do. They go where life is good. They reproduce as much as they can, right? So that cognitive light cone, that is the thing that I’m talking about that scales. And so when we are looking for life, I don’t think we’re looking for specific materials.”

How to Bring Back Belonging – via The Beautiful Truth

Key quote: “When belonging fractures, new patterns of leadership often emerge. At Airmedtec, influence no longer followed hierarchy alone. People who had never seen themselves as leaders began to take responsibility for stabilizing teams and rebuilding routines. Small acts like mentoring a colleague or reviving a tradition became the scaffolding for a new culture. This redistribution of leadership sent a clear signal: belonging is not something handed down from the top, but something built collectively. As teams found new rhythms, the sense of shared responsibility deepened.”

This article Why we overcomplicate things is featured on Big Think.

Arianna Huffington moved to England from Greece at 17, earning a master’s degree in economics from Cambridge University where she was president of its celebrated debating society. Best known as the co-founder of The Huffington Post (now HuffPost) — acquired by AOL in 2011, then by BuzzFeed in 2020 — she became a force in shaping the digital media landscape and a fixture on various “most influential” lists. She has also written 15 books.

In common with other seemingly indefatigable high-achievers, Huffington faced a physical reckoning in 2007, when burnout caused her to collapse “into a bloody mess.” Along with a fractured cheekbone she sustained a revelatory crack in her outlook on life and began to map out a significant pivot.

Now, as founder and CEO of Thrive Global, she has redirected her entrepreneurial focus towards well-being — and with Thrive AI Health, created in 2024 in partnership with OpenAI, she wants “to use AI to make expert-level health coaching accessible to everyone.”

Here, she joins Big Think to explore the public health crisis in the US, the women at the core of AI, why humans are the ultimate “agents,” and much more.

Big Think: What do you wish you’d known when starting your first business?

Huffington: The idea that burnout is the price we must pay for success is a delusion. Had I known that I would have spared myself a broken cheekbone when I collapsed from exhaustion two years after I launched HuffPost.

After that wakeup call in 2007, I became passionate about the science that links our health and our daily habits — food, exercise, sleep, stress management and connection with others. Fortunately, I was able to course-correct long before founding my second company. And when I started Thrive Global in 2016, elevating these daily behaviors as fundamental to our health and productivity became the foundation for the company.

But as clear as the science is, it’s amazing how stubborn the delusions about burnout culture are. In fact, there’s been a wave of recent articles about the return of “996” culture, which has its roots in the Chinese tech scene and refers to working from 9 a.m. to 9 p.m. six days a week. 

So unfortunately, more people are going to have to learn the same lesson I did. But culture change is possible. Look at cigarette smoking. In 1965, over 42% of American adults smoked. Today it’s around 12%. It can take a while for the culture to catch up with science — but the science between our health and our daily behaviors is as clear as the science between our health and smoking.

Big Think: You started The Huffington Post in 2005, with celebrity contributors as well as unpaid bloggers a central part of the content strategy. What do you make of the media landscape now?

Huffington: What I loved about HuffPost was the ability to have a direct connection to individual voices and how that could create community and a two-way conversation. And what we’re seeing with the rise of Substack, for example, is that it creates both a space for communities to coalesce and a viable business model. 

There are, of course, huge challenges, especially among legacy media institutions, but the hunger people have to tell their stories, to have a direct connection to writers, and to be part of a community has never been stronger. It’s just finding and driving different models.

Big Think: You contributed to Big Think several years ago. What kinds of shifts have you seen in the business world since then — any epiphanies?

Huffington: We’ve reached this pivotal moment when we’re finally beginning to see our daily behaviors not as soft, fuzzy perks, but as medical interventions. And this is happening as employers are increasingly alarmed about the costs of healthcare, which are projected to go up by 9.5% next year.

What business leaders are seeing is that the chronic disease crisis is also a workplace crisis. The Harvard T.H. Chan School of Public Health found that 58% of U.S. employees have a chronic health condition, and over three quarters of those employees need to manage those conditions during regular work hours.

There’s been a huge shift in the business world as leaders realize their importance as stakeholders in fixing our broken healthcare system. And given that over 60% of the population gets healthcare from their employers, and we spend one-third of our lives at work, the workplace can be a significant place to transform healthcare. 

Big Think: Some people retire before they are 70, but you’ve chosen to continue to work. What’s your working routine?

Huffington: Actually, I find that in my 70s, I have many advantages over my younger self! One thing I learned is how draining all my worries were and how much energy you lose at the beginning of your career by looking over your shoulder for approval.

I’m also very blessed to love what I do. As long as I take enough time to recharge, sleep, meditate, exercise, be with the people I love, I don’t really separate my work from the rest of my life. I actually don’t remember the last time I didn’t work at all over a weekend!

In any demanding job, it’s impossible to do everything you could have done in any one day. So I have learned to declare an end to my working day by being comfortable with incompletions and turning off my phone and making sure it’s charging outside my bedroom. Our phones are, after all, repositories of everything we need to put away to allow us to sleep — our to-do lists, our inboxes, the demands of the day.

And one place you won’t find me at is a breakfast meeting! My least favorite way of starting the day.

One of my favorite lessons when I was studying economics at Cambridge was about opportunity cost: when you say yes to something, it means saying no to everything else. It’s not just important in an economic sense — it applies to every decision we make in our daily lives. For instance, if I say yes to a breakfast meeting that drains me, I’m also saying no to the things that actually help me be productive and creative and engaged. Being able to say no — without apology — is an essential skill. As I often say: “No” is a complete sentence!

Big Think: Who do you admire and why?

Huffington: I love and admire [philanthropist] Alice Walton. For decades we’ve heard the mantra that nothing can be done to fix the healthcare system. But Alice is determined to prove that wrong. With the Alice L. Walton School of Medicine and the Heartland Whole Health Institute, she is transforming healthcare in the heartland by combining art, nature and a whole-human approach.

As much as 80% of medical education focuses on biology, while 60% of premature deaths are due to behavioral factors. So by training a new generation of doctors on prevention and the health impact of our daily behaviors, she’s changing the healthcare system from the inside.

One thing I especially love about Alice is how when she’s excited about something, she has to share it. That can be on an individual level, or, given her love of art, it can mean building a world-class museum in rural Arkansas and making admission free. And she brings a sense of urgency to her most important mission: fixing our healthcare system.

And it’s hard not to mention my mother. Her motto was that failure is not the opposite of success but a stepping stone to success. And that idea of embracing failure, combined with her unconditional love, made it possible for me to take many risks throughout my life. There isn’t a single corner of my life that isn’t filled with her spirit.

Big Think: What do you think leaders need to focus on in the age of AI, and why — how are they doing with that?

Huffington: Leaders need to go into the age of AI with eyes wide open. We now have the benefit of hindsight and we should use it. That means recognizing the negative downstream effects of previous technologies.

There’s growing anxiety about AI in the workforce. A recent Pew poll found that in the U.S., only 10% of people are more excited than concerned, while five times that number — 50% — are more concerned than excited, up from 38% in 2022. 

Leaders need to ensure not only that their companies have a new technology infrastructure — they need their employees to have a strong personal infrastructure. The core of that is resilience, which is really the most important skill we are going to need to navigate the age of AI. 

The limiting factor of AI won’t be data centers, or energy, or chips, or computers. The limiting factor will be what it has always been throughout human history: human nature.

Big Think: You partnered with OpenAI to launch Thrive AI Health in 2024. How is that going, and what can we expect from the company in 2026?

Huffington: Our mission has always been simple but ambitious: to use AI to make expert-level health coaching accessible to everyone. We want to help people know what to do to live healthier, longer lives, and follow through on it.

Since launch, we’ve gone from early alpha testing to a live beta. Every day, people are using the Thrive AI Health Coach to build small, sustainable habits — such as taking a stretch break after a meeting or a short walk during lunch, actions that fit seamlessly into everyday routines, including work. People are also giving us valuable feedback that’s shaping the next generation of the product.

As we move into 2026, the focus will be on deepening engagement and preparing for scale. That means translating the data and goals people get from their wearables or healthcare providers into daily micro steps they can do with the right nudges and support.

We’re also building with our partners — including Eli Lilly, Hilton, the University of Pennsylvania and the Yale School of Public Health — to ensure the product works across different populations and contexts.

Big Think: Silicon Valley is primarily accelerationist when it comes to AI; Europe is more precautionary — where do you stand?

Huffington: I’m still placing my bets that AI can be a force for good. But it remains an open question. Right now, we’re at a fork in the road, where two different futures are possible.

One is that AI turns out to be just a more powerful version of the technologies that have come before — a more effective way to mine our attention, polarize us, and distract us from what matters.

The other scenario is that now that AI has dethroned human intelligence, it becomes a forcing mechanism for a question we’ve been avoiding since the Industrial Revolution: what does it mean to be human? 

Now that Descartes’ “I think, therefore I am” no longer holds true, this dethronement could be the greatest gift, finally forcing us to rediscover the possibilities of our full humanity and reconnect with the infinity of our inner selves — soul, spirit, consciousness — and the awe and wonder of who we are: “spiritual beings having a human experience” as [philosopher Pierre] Teilhard de Chardin put it. 

So the question is: how can we use what AI can do to augment what it can’t do? That, to me, is the ultimate possibility of AI. The real existential threat of AI isn’t that it turns on humans and destroys the human race, it’s that humans won’t keep pace with AI by investing in our own growth and evolution. 

[Author and historian] Yuval Noah Harari summed it up: “If for every dollar and every minute that we invest in developing artificial intelligence, we also invest in exploring and developing our own minds, it will be okay. But if we put all our bets on technology, on Al, and neglect to develop ourselves, this is very bad news for humanity.”

Agents are the AI concept of the moment, but we’re the ultimate agents, and AI will be what we make of it.

Big Think: Does the dominance of the tech “broligarchy” jeopardize a safe and prosperous AI future?

Huffington: There are of course many players from the classic Silicon Valley bro culture who dominate the headlines, but there have also been many pieces recently about a resurgence of spirituality in Silicon Valley — something I’ve seen first-hand among friends I have there, including on the Thrive board.

And there are many amazing women building AI. There is, for instance, Fei-Fei Li, who’s sometimes called the “godmother of AI” and spent 20 years doing pioneering work in AI. She’s now a professor at Stanford and the co-founder of Stanford’s Raise Health program, which is helping to establish a framework for how AI can be used in health safely and equitably. As she wrote in her memoir The World’s I See, “I believe our civilization stands on the cusp of a technological revolution with the power to reshape life as we know it. To ignore the millennia of human struggle that serves as our society’s foundation, however — to merely ‘disrupt,’ with the blitheness that has accompanied so much of this century’s innovation — would be an intolerable mistake.”

And there’s Fidji Simo, OpenAI’s new CEO of Applications. She published an internal manifesto recently outlining how AI can be used to empower people, especially in improving their health, writing, “Every major technology shift can expand access to power—the power to make better decisions, shape the world around us, and control our own destiny in new ways… We can start by making sure the keys to empowerment and opportunity — knowledge, health, creative expression, economic freedom, time, and support — are widely available.”

This article “AI can be a force for good”: Arianna Huffington on work, health, and our future is featured on Big Think.

Leaders need to invite disagreement, not just expect it. When the invitation to offer their opinion is not clear, teams will assume you don’t want it. Leaders often don’t realize that their status can unconsciously silence dissent. No matter how often leaders stress that no one will be punished for disagreeing, their own zeal, conviction, intelligence, and energy can be intimidating.

The classic American “open-­door policy” isn’t enough to draw out dissenters. Leaders need to create conditions that empower their teams to speak up. When it has been the cultural norm to be deferential to power, or when disagreeing has been conflated with being disagreeable, leaders need to work even harder to change the norm.

One leader we worked with in Singapore, Mei Chen, developed a creative way of drawing out her people’s best ideas, even when they contradicted her own. Mei had received some bracing feedback during her Bonfire Moment. Her team said that she always responded to objections with defensiveness. We could see that she was brilliant but very measured in how she shared ideas. As we discussed these issues, Mei realized that some of the behaviors that had made her effective in her prior job as an analyst were now hurting her ability to lead. She would take a few days to develop any new proposal before bringing it to the team. She had so much of herself invested in her ideas that when they got any pushback, it felt like a personal attack on her capabilities.

Mei recognized that she needed to change before the most talented people on her team quit in frustration, and she found two tactics that worked very well. First, she committed to come to meetings less prepared, despite her perfectionist instincts. She found that when she put less prep work into her own ideas, it didn’t sting as much when people raised questions and outright disagreed. Second, to further encourage honest exchanges, she started having more one-­on-­one conversations. In private with a single team member, she would lay out her idea and the reasoning behind it. Then she’d say: “I’m pretty sure I’m missing out on important things here. What’s missing? What don’t I see?”

Mei’s approach aligns with the conclusions of researchers from Singapore Management University and UC Berkeley. Their study suggests that when leaders frame conflicting ideas as expressing disagreement, team members assume the leader isn’t really open to dissenting opinions. This assumption tends to shorten conversations and reduce the exchange of important information. But when the leader frames a conflict as a debate, with fair treatment for all sides, people assume that dissenting opinions are wanted and expected. In the study, the amount of shared information increased fourfold when the leader simply changed their language.

The good news is that it really is possible to escape the trap of the inner circle. Anil Sabharwal, who led the team that built Google Photos into a multibillion-­user app, describes his approach this way: “Debate. Argue. Get into it. The best results come from passionate, constructive, positive contention. Encourage it. Even force it. But know it requires a foundation of trust, honesty and respect. If you don’t have that, you just get pure contention, and that ain’t good.”

Improving the quality of your disagreements 

Tech pioneer Bob Taylor, who built one of the dream teams of the computing revolution, was a master at fostering productive disagreement. In the late 1960s, he played a key role at the Pentagon’s Defense Advanced Research Projects Agency (popularly known as DARPA), which launched the earliest version of the internet, connecting computers at the Pentagon, MIT, UC Berkeley, and a research lab in Santa Monica. Then in the 1970s, he led the team that invented the world’s first personal computer at the Xerox Palo Alto Research Center (PARC). As former Google CEO Eric Schmidt observed, “Bob Taylor invented almost everything in one form or another that we use today in the office and at home.”

Visitors to Xerox PARC were often shocked at the intensity of arguments among Taylor’s researchers. They didn’t try to win debates for the satisfaction of proving each other wrong; the goal was always to illuminate each other’s ideas. Criticizing a colleague’s personality or character was not allowed. Taylor saw his main role at the lab as preserving this collaborative culture and protecting it from the lone-­wolf tendencies of some of his smartest researchers. He frequently quoted a Japanese proverb: “None of us is as smart as all of us.”

Taylor liked to explain that there are two kinds of disagreements. In “Class 1” disagreements, neither party truly understands each other’s point of view, so they resort to straw man arguments —­ distorting an opposing opinion into an exaggerated, easily rebutted version of itself. Class 1 disagreements erode morale and degrade a team’s effectiveness over time.

During “Class 2” disagreements, in contrast, people take the time to fully understand each other’s point of view, to the standard that “I can explain your point of view to your satisfaction.” By doing a deep dive into someone else’s position, finding the strongest aspects of it, explaining it back to them, and only then formulating a rebuttal, you have to create a “steel man” argument instead of tearing down a straw man.

Taylor saw his role not as judging disagreements but as moving his people from Class 1 to Class 2 disagreements. He was able to bring together brilliant minds representing a vast array of expertise and backgrounds. More impressive, he created an environment that maximized the benefits of that diversity and essentially took himself out of the inner circle.

Taylor’s instincts a half century ago confirm what we know today about high-­quality disagreements and the power of diversity. In an experiment by the Kellogg and Stanford business schools, respondents were put in socially diverse and nondiverse teams. At the end of a team-­based performance task, they were asked to predict how well they did. By comparing each team’s self-­perceived effectiveness with its actual performance, the researchers found that we incorrectly assume that working with similar people will lead to better results. In fact, socially diverse groups had better results, even though they found it harder to debate their opinions because they didn’t share as many references and vocabulary.

This article Collaboration masterclass: How to wrangle disagreements like Bob from Xerox is featured on Big Think.

What do trees and businesses have in common?

For one, they’re systems. A tree dies when you starve it of water and sunlight, just as a business collapses when its resources are drained. In fact, all living things are systems: plants and animals, yes, but also organizations, relationships — even friendships. Stop responding to texts, and the relationship withers. (Unless, of course, that’s the goal. As the Gen Z might say: ghosting.)

Systems rule the world, and once you see them, you can’t unsee them. Perhaps I’m thinking about them now, as we slip into the holiday season, when our own network of plans, friendships, and families seems to weave together. (The system breaks, it turns out, when you forget the baby’s diapers at a family function.)

On this subject, no one is better than Donella Meadows, the environmental scientist and systems theorist whose essay, “Dancing With Systems,” is perhaps the most practical way to think about life, business, investing, work, and family.

Every so often, I find myself returning to her 14 rules, and as the year winds down, I want to share them here. “We can’t control systems or figure them out,” Donella writes. “But we can dance with them!”

Key quote: “I will summarize the most general ‘systems wisdom’ I have absorbed from modeling complex systems and from hanging out with modelers. These are the take-home lessons, the concepts and practices that penetrate the discipline of systems so deeply that one begins, however imperfectly, to practice them not just in one’s profession, but in all of life. The list probably isn’t complete, because I am still a student in the school of systems. And it isn’t unique to systems thinking. There are many ways to learn to dance. But here, as a start-off dancing lesson, are the practices I see my colleagues adopting, consciously or unconsciously, as they encounter systems.”

“Oh, to be 86 again”

What does a lifelong learner do the year before he dies at 99?

Duh: he keeps learning.

I loved this portrait of Charlie Munger’s final years. He was a man who, even at 98, was making contrarian bets, asking big questions (“Does Moore’s Law apply in the age of AI?”), and forming new friendships.

Munger never had much interest in retiring to a life by the ocean, though he maintained a sprawling estate in Montecito with view of the Pacific. Instead, he hung out around his modest house in Pasadena (no A/C), and spent his final days trading coal stocks, backing young real-estate investors, and debating ideas with his breakfast club.

Simply: he kept is mind working even when his body didn’t. He read, he questioned, he taught, and he listened (well, to the extent he could; his hearing was terrible). The profile offers a moving inquiry into the nature of longevity: stay curious, stay connected, and keep asking questions right up until the end. (H/T Nima Shayegh)

Key quote: “‘Even a week or two before passing away, he was asking questions such as, Does Moore’s Law apply in the age of AI?’ recalls his friend Jamie Montgomery, referring to whether artificial intelligence would see exponential gains like those experienced in computational power. Friends and family say Munger’s eventful last period offers lessons for investors — and a blueprint for how to age with grace, equanimity and purpose. ‘To the day he died, that mind was running,’ says Munger’s stepson, Hal Borthwick. ‘He never stopped learning.’”

A few more links I enjoyed:

The Progress Paradox – via Matt Prewitt (Noema)

Key quote: “Plenty of readers will still be skeptical. To many, the cutthroat Silicon Valley startup ecosystem proves on its face that competition, not monopoly, produces innovative technology. But look closer. Silicon Valley startups are routinely valued at many multiples more than their revenue or profits straightforwardly justify. What explains that? Investors are betting that those startups will eventually either become monopolies or merge into existing ones through acquisitions or other forms of financial consolidation. In other words, technology startups are ‘competing’ not to serve consumers’ needs, but to become — or join — monopolies. The aim for many of today’s Silicon Valley startups is a high-profile acquisition and a lucrative ‘exit.’”

Iain McGilchrist: Re-enchanting the Brain – via The Beautiful Truth

Key quote: “‍As societies grow, it’s harder for people to know and trust each other. Governance requires distance and bureaucracy, leading to a more crude control mechanism that replaces the full complexity of life. This simplification becomes visible in art, architecture, poetry, music and thought. I know I’m glossing over a lot, but this shift aids the left hemisphere’s desire for simple answers that give us power. Power is, in a sense, its main motivation. It controls our right hand, enabling us to grasp and hold, whereas the right hemisphere manages a complex, rich picture.”

This article The 14 rules for navigating complex systems is featured on Big Think.

What is the fundamental human skill? It’s riding the crest of change and embracing the unknown, says Tim O’Reilly, tech trend visionary and CEO of O’Reilly Media. He “imbibed” this idea from sci-fi writer Frank Herbert (Dune) in the 1970s and has been “surfing the edge” of change ever since.

Tim founded Global Network Navigator (GNN), the first ever commercial web portal, and sold it to AOL in 1995. He went on to become one of Silicon Valley’s most influential thinkers, known for popularizing the phrases “open source” and “Web 2.0” among others. O’Reilly Media — which Tim started over 40 years ago — has provided business and tech training to millions of users and currently serves over 5,000 companies worldwide.

He has long argued that technology can create new jobs rather than laying people off – it’s a key theme in his 2017 book WTF? What’s the Future and Why It’s Up to Us. Despite evidence this year that AI has been wiping out jobs, especially for younger workers, Tim is optimistic people can adapt quickly and flourish in an automated world. 

So we asked him — how can humans stay ahead of the game?

Big Think: Rather than creating employment, AI is in some respects killing it, according to 2025 Stanford research. What’s gone wrong?

Tim O’Reilly: AI is not taking jobs: the decisions of people deploying it are. We’re concentrating value in the hands of people who use it to gamble on share prices. Venture capitalists (VCs) are treating companies like financial instruments they can trade for a profit by consciously whipping up a media bubble rather than fostering real growth. These Silicon Valley hype-meisters should be ashamed of themselves.

The message in my book WTF is more relevant than ever — to look at this extractive pattern and do something about it now. If we valued companies that made real-world profits and reinvested them in solving problems — such as caring for aging populations, reducing working hours, advancing medicine — AI would create opportunities and new work.

One solution is to lower taxes for employing people, rather than automating them out of existence, and raise taxes on extractive capital gains.

Big Think: Should corporate leaders be responsible for protecting jobs from AI and how can they do that?

Tim O’Reilly: A society that uses automation to “build shareholder value” while impoverishing everyone else will not be successful. Some companies have trumpeted how they’ve used AI to lay off employees. They’ve missed the point. If you replace humans with AI, it won’t make customer service better. You have to integrate the two to improve service.

Corporate leaders can start by listing all the things you wish you could do for your customers, but couldn’t afford. For example, at O’Reilly, we’re using AI to increase the content we can translate, into many more languages than before, increasing our addressable market. We are building more personalized learning paths for users, and creating tools for corporate customers to accelerate their employees’ skills and career paths.

In our business, we’ve experienced headwinds from potential customers saying “we won’t need to train programmers because AI will do the job.” The hiring prospects for junior programmers looked bleak for a while. But we’ve recently seen reports that even frontier labs are hiring junior programmers again, because they are quick to become more productive with the new tools.

Big Think: Is an AI-led market crash imminent?

Tim O’Reilly: The stock market is overvalued and due a correction. But bubbles can last longer than you think. After selling GNN for cash and stock options, I thought I had to get out quickly and realized my options for a tiny fraction of the $1 billion they would have been worth at the peak, a few years later. So the bubble will go for a while yet. But I do worry about some of the massive investment commitments that have been made.

Big Think: How might the bubble finally go pop?

Tim O’Reilly: A lot could go wrong. The immense energy requirements of AI could lead to delays. Reluctant people will bet on their old business models. For example, at GNN we tried to get book publishers and phone companies to take the internet seriously, but they wouldn’t, or took several years to start thinking about it. 

Some US AI labs have pushed this narrative that we need massive investment to reach artificial general intelligence (AGI) [which can perform any task a human can], or super intelligence [AI surpassing human brain power] — with the notion that it’s a winner-takes-all race. But Chinese businesses are showing you can build models very close to state-of-the-art for much less. US companies counting on monopoly rents would instead compete in a commoditized market, making it impossible to ever recoup their costs.

Big Think: What are the biggest pieces of nonsense about AI you’ve encountered recently?

Tim O’Reilly: The idea that previously software was a tool, now it’s a worker. That’s been true for a long time — software was doing work long before AI. It’s all in service of the narrative that this technology is different from anything before it.

Another big nonsense is how imminent AGI is — or “AI becomes super intelligent and, bang, everything is different,” with complete lack of real-world perspective or understanding of what needs to happen first. AI is not singular, it’s “normal technology.” There are still laws of physics, things to be built, and many constraints. We have a long way to go.

Big Think: What’s your advice for business leaders in this hype-led environment?

Tim O’Reilly: AI is a transformative technology, and we will learn how to make money with it. So you have to invest. But figure out how it really impacts your business and don’t buy the hype. Plan scenarios and develop a robust strategy that can survive in multiple circumstances. When the bubble bursts, you want to be making things that genuinely improve your customers’ lives. The market will eventually reward people doing the real stuff.

Big Think: What effect does the rise of “post-literate” media like YouTube and TikTok have on the learning environment?

Tim O’Reilly: YouTube is a fabulous resource but doesn’t live up to its potential for learning content because of its advertising model. For example, most videos contain five minutes of filler so they can be long enough to attract advertising. We’ll see this friction with large AI companies too if they settle on an advertising model. It’s one of the things I worry about most with artificial intelligence.

Big Think: You co-lead the AI Disclosures Project, which addresses societal dangers from commercialization of the technology. What are the challenges and how can we manage them?

Tim O’Reilly: Platforms monetize attention, often in ways that are against users’ interests. We remember when social media sites were good for following your friends and family. Now they’re continually pushing other stuff at you.

The project initially asked: What if companies had to disclose metrics, as part of their SEC reporting, showing how their platforms operated before this process of “enshittification”? There’s a whole lot we could be doing to make AI protocols that promote safety and the interests of users, not just providers.

Also, in copyright, we’re trying to figure out if protocols can say, “You want to use my content? Pay me for it.”

Big Think: In the 1970s, you wrote a book about science-fiction author Frank Herbert. Are any of his ideas still relevant to you today?

Tim O’Reilly: All of them. Herbert’s famous line from the novel Dune is still true: “Fear is the mind killer.” Realizing the future does not have to be like the past and trying to keep it like the past is almost always the wrong strategy. In technology, that means being comfortable with the unknown. 

Take those trying to build walls against the AI wave, pretending we can keep it at bay. Or walls against other changes, such as mass immigration, climate change or the aging population, rather than seeing the opportunities. The longer you spend putting up walls, the harder the crash when they fall.

Surfing the edge of change today requires balance and responsiveness. With AI, there are no standardized rules — you have to figure out where the edges are for yourself and your business. This is “get yourself dirty” work, not something you can do from a distance.

It’s an intensely creative period where we solve existing problems in new ways, and new problems that we couldn’t handle with previous tools. The answers are different every day because they are still being invented. The invention, diffusion, and learning are a social process.

For example, there’s a temptation to try to fit AI into existing workflows. When apps were invented, “mobile first” — design the new thing first, then retrofit the old thing when you understand it — became the watchword. I keep urging my company to think “AI first” — try to do the job better in new ways. When you’ve figured that out, go back and improve or integrate with what you did before.

This article “Surfing the edge”: Tim O’Reilly on how humans can thrive with AI is featured on Big Think.

A great way to start taking stock of your culture is to review your written artifacts, including your mission, values, and purpose statements. You’ll also want to gain a clear understanding of the roots and history behind these statements. How did you get to where you are now? Do you feel like your mission statement and written values align to how your people show up every day? Leaders with a clear understanding of these fundamental documents and histories can use them as guideposts as they build culture; or they can work to change them to better represent the culture they want to intentionally design.

Brenna Davis, CEO of the nation’s largest organic produce wholesaler, Organically Grown Company (OGC), emphasizes the importance of honoring the company’s roots. She has a reverence for the founders and retired alumni of the company, who at OGC are referred to as “elders.” Davis told us about how the company has upheld its founders’ mission by establishing the first perpetual purpose trust in the country. Founded in 1978 by a group of farmers, OGC has long centered its work on its mission: to promote and inspire the growth of the organic agriculture movement. In 2018, it took that history into account when it restructured to transfer ownership to the Sustainable Food and Agriculture Perpetual Purpose Trust. This allows OGC to focus on its long-term purpose instead of short-term shareholder returns. With this act, purpose and culture became inextricably linked and inextricably tied to how the business runs. Now, the organization’s relationship to its purpose affects how work gets done, how meetings are run, and how decisions get made. It is by understanding its history that OGC was able to carve out a path for its long-term future.

For you, revisiting your company’s mission, purpose, values, and history may spark some initial ideas about where there may be gaps between your purported culture and how it shows up. When you compare your mission statement or documented values to organizational policies and tactics, do they match? You may find that you’ve strayed too far from your original purpose and that you need to find a way back.

History can also reveal how far you’ve come. For legacy companies, there will often be lessons of what not to do and clues as to how to reconcile past wrongs with your vision for the future. Whether it is ties to systems of oppression or dishonest business practices, history is not to be forgotten or swept under the rug, but to be learned from. Follow those hunches to the next part of your dig and document them as you design questions for surveys, interviews, or roundtables that you may conduct later. Keep your personal notes handy in a spreadsheet, Google Doc, or a good old-fashioned legal pad. The most important thing is keeping track of the work you’re doing here in a way that will be most accessible to you and your team when it’s time to go out and talk and listen to your stakeholders. 

Next, explore rituals. From the way you start and end meetings to how you celebrate employee contributions to your annual company retreat, rituals are the day-to-day traditions that reinforce your culture. At meetings, people have used everything from a routine check-in to see how their team members are feeling to a moment of gratitude shared at the onset of meetings.

At The Honest Company, CEO Carla Vernón aims to create psychological safety and creativity, so at town halls she asks employees to share which of a preselected set of Pixar characters represents what mood or energy they’re embodying in the moment. (The options include the characters of Joy or Fear from Inside Out, or the relaxed optimism of Crush the Turtle in Finding Nemo.) And to foster a culture of open communication, Lee Wallace, CEO of fair trade coffee producer Peace Coffee, has instituted an agenda item in every meeting that asks what needs to be communicated and to whom as a result of the meeting. Keeping everyone on the same page with the information they need to succeed is a key part of her change management agenda as she grows the company. Simple activities repeated over and over have a cumulative effect on culture.

Many of you may be thinking, That kind of stuff is not for me — you don’t see a connection between results and asking people how they feel at every meeting. We know it works and encourage you to be vulnerable and get out of your comfort zone and try it. But also recognize that not all rituals are about feelings. Other types of rituals that reinforce culture are brief weekly check-ins about the successes and challenges of the week, a daily review of a tenet of centering customer needs at staff meetings, or a biweekly cross-functional review and prioritization of issues that have come up during those two weeks.

Celebratory rituals often take the form of a company picnic or holiday party, but consider what it would mean to create a ritual that was uniquely suited to your company and colleagues. OGC has long celebrated its annual tomato war, where team members go out at the end of tomato season and throw overripe tomatoes at each other. When we heard about this, we immediately wanted an invite to the event, and it struck us that most companies don’t have celebrations with quite so much personality and distinction. The fact that OGC does signals to us that that culture is being intentionally designed. Do you have any traditions that are uniquely suited to your culture? Do you have any ideas for new traditions you could institute? This is the intentionality piece of building culture through rituals. None of the examples we give you here will be appropriate to you, but they should show you how specific and apt to the culture your rituals can be.

This article Find your own tomato war: How to fortify culture through ritual is featured on Big Think.

Ever since I first read Janine Benyus’s Biomimicry: Innovation Inspired by Nature, I’ve descended into a rabbit hole in search of what “intelligence” really means (and who has it). Perhaps that’s why I love the name of this newsletter so much. [It’s a worm, after all. A humble, indispensable critter buried beneath the soil.]

Benyus’s central argument is that the “smartest” solutions to human problems already exist in nature. We just need to know where, and how, to look for them. (For instance: wind turbines inspired by humpback whales.)

So perhaps it’s no surprise that I was riveted by a recent piece on monarch butterflies. The piece explains how monarchs actually navigate: with two independent compasses built into their biology. On clear days, they orient themselves using a solar compass. But when clouds roll in or weather becomes unpredictable, they switch to a backup magnetic compass that reads ultraviolet light to sense the angle of Earth’s magnetic field.

In other words, they’ve evolved slack into the system, a subject I explored in greater detail earlier this year. For butterflies, just like humans or organizations, redundancy — not optimization or efficiency — creates the conditions for long-term survival.

Key quote: “Monarchs have evolved two highly sophisticated navigational systems. Most of the time, they rely on a system that orients them in relation to the sun, keeping them pointed south throughout the day by compensating for the sun’s movements across the sky. When clouds get in the way, monarchs switch to a backup compass that relies on ultraviolet light to detect the angle of the Earth’s magnetic field. Their twin compasses usually keep migrating monarchs headed in the correct general direction.”

Culture, not AI, is the real engine of progress

This week, Big Think released its Engines of Progress special edition, which I highly recommend.

My favorite piece comes from Beatrice Erkers, a writer and podcaster who helps lead the Existential Hope project.

Beatrice’s essay makes the case that while hundreds of billions of dollars are flowing into the physical infrastructure of artificial intelligence — data centers, power plants, chips, etc. — there’s an even more powerful force we consistently overlook: culture.

“We usually think of infrastructure as bridges, satellites, and fiber-optic cables,” she writes. “But beneath steel and concrete lies something less tangible but just as powerful: culture — the stories and symbols that make some futures seem absurd, others inevitable, and a few worth building.”

Key quote: “Culture is infrastructure, yes, but it is infrastructure that’s more like weather than bridges: largely unpredictable and sometimes destructive. And this volatility is precisely why the tone of culture matters. Left unchecked, fear and cynicism fill the space. To channel culture toward constructive ends, we need something sturdier: hope.”

A few more links I enjoyed:

How business leaders can help solve the world’s toughest problems -via HBR

Key quote: “‍Rosabeth Moss Kanter, professor at Harvard Business School, believes the world demands a new kind of business leader. She says so-called “advanced leaders” work inside and outside their companies to tackle big issues such as climate change, public health, and social inequality. She gives real-life examples and explains how business leaders can harness their experience, networks, innovative approaches, and the power of their organizations to solve challenging problems.”

In a digital world, reading printed media has become more important than ever – via Monocle

Key quote: “Perception and knowledge always develop in interaction. The abstract knowledge that we acquire can shorten our path to an enriched experience, diversifying and intensifying our engagement with the sensory world. The apparent richness of the external world is in reality the richness of our inner world. Think of an exhibition: only those with knowledge of cultural history can fully comprehend the wealth of associations that it offers.”

This article What we can learn from butterflies is featured on Big Think.

There’s an old adage that goes, “No one ever got fired for hiring [insert consulting firm here].” This rang true for many years, as there was no substitute for consulting ‘SaaS’ (‘scapegoat as a service’) — but a reckoning is coming.

After nearly a decade of uninterrupted growth, the days of multi-million-dollar, multi-year contracts with governmental entities and private companies are swiftly withering away. Firms that succeeded in convincing organizations they couldn’t solve their own problems are finding their claims, value, and costs are being called into question.

In just the last few months, Pricewaterhouse Coopers let go 1,500 US workers (2% of its US workforce), following 1,800 cuts in 2024. Accenture announced a round of 19,000 layoffs in 2023, with ongoing reductions continuing. KPMG laid off 4% of its US audit workforce, citing pressure on profit margins. Deloitte, Booz Allen Hamilton, IBM, and Guidehouse were pressured to justify their fees by the Department of Government Efficiency. Microsoft has even completely halted hiring in its consulting business.

And it’s all because there’s a new sheriff in town — AI.

I’ve said before that consulting is bullshit. The industry has always been a strange hybrid of insight and illusion. At its best, it’s about uncovering what organizations can’t see in themselves to identify the hidden drivers of growth. At its worst, it’s been about glossy decks, generic frameworks, surface-level diagnoses dressed up as strategy, and most commonly, cutting heads.

Now, with AI, CEOs are starting to do the math. AI can process mountains of information faster than any human ever could. It can benchmark, categorize, summarize, and even recommend. So theoretically, if an initiative used to cost $500,000 to implement with in-house resources, and machine learning can drop that cost down to $10,000, where does a $100,000 consulting fee fit in the equation? It doesn’t.

Additionally, organizations can augment their existing workforce through AI, raising their employees value through increased output and efficiency. It then becomes a dual advantage — companies can get more work, faster work, out of fewer employees, without the exorbitant consulting fees. And, boy, do CEOs like to save money.

And, because the bulk of billable consulting hours comes from the reams of research, analysis, and reporting they produce, the potential for the obsolescence of traditional consulting is very real. The classic ‘pyramid’ staffing model which relies on a large base of junior staff to support a few senior partners will cease to exist. Traditional labor-intensive business models will be stressed to the brink by more nimble technologies. 

Of course, there’s always a point where we’ll need an expert. Outside of the corporate world, it’s maybe a plumber to handle a sticky situation with a backed-up sewer line. Or an electrician to wire up a few new outlets and a security light. We hire experts because they provide a skillset and experience that we don’t have or don’t see value in learning. In the business realm, the ‘expert’ will be the one who provides insights, expertise, and unique value that is economically unreasonable to source any other way. This means that, in short order, the industry will be split into two camps:

• AI-Driven Analysts — Firms that specialize in automation, process modeling, and data-heavy recommendations for highly complex environments.
• Human-Centered Enablers — Consultants who understand organizations as living systems and help people adopt and sustain change.

The first group will desperately fight for relevance against AI tools that are faster, cheaper, and tireless. The second will redefine what consulting means. The death knell will quickly sound for those who refuse to take either position because, ‘this is the way we’ve always done it.’

Viva la (value) revolution

AI is brilliant at collecting what’s obvious, but most organizational problems aren’t obvious. They’re layered, contextual, and full of contradictions. 

Yes, AI can tell you that sales are down 15%. It can show which regions underperformed. But it can’t tell you that customers stopped trusting your messaging because your sales team quietly changed its tone after leadership turnover. It can’t recognize that your marketing team’s ‘alignment meetings’ are just polite battles over conflicting goals. It doesn’t know what’s missing from the data or which questions matter most. 

That kind of understanding doesn’t come from quantitative data or AI. It comes from experience. The kind of ground-level, hands-on work that real experts have spent years, even decades, doing inside real organizations. A real expert can decipher an unclear value proposition, misaligned incentive structure, or a toxic internal culture masquerading as a revenue problem. A real expert can recognize the subtle threats and risks AI has no transparency to — the language people use in meetings, the gaps between stated goals and actual incentives, or the moment when employees stop speaking up. 

This is where traditional consultants have dropped the ball for years. They perceived their value in the data, rather than in insights, observations, and human understanding. They got lost in the jargon and gobbledygook of notoriously vague ‘consultancy speak.’ And with the AI revolution underway, it will be easy for these firms to continue to mistake information for understanding when they have a clean and comprehensible output from an LLM. 

But without context, it’s like a map without a legend — it shows you where things are, not what they mean. AI can point to the “what” and “how,” but it can’t discern. Only humans can unravel the “why.” That’s what makes the next era of consulting profoundly human — the ability to see beyond what’s presented, interpret nuance, and make sense of the contradictions that define real organizations. 

AI will quickly and ruthlessly trim the fat, strip away all illusion, and force the industry to return to its true purpose: helping organizations think better and change for the better. The firms that survive this transformation will be those which seamlessly blend technological fluency, human understanding, and deep contextual insight. Because consulting should never have been just about reports, slide decks, and billable hours. It should always have been about knowing what to look for, what to question, what to ignore, and helping leaders be better decision makers. 

But what do I know? I’m just a consultant.

This article Will AI decimate consulting? is featured on Big Think.

Richard Branson told me the single most important lesson he wished he’d learned at the beginning: the willingness to turn setbacks into superpowers and wounds into wisdom. It was hard to imagine his earlier challenges while sitting with him in the hot tub on his private island in the Caribbean, but there have been many that he’s not only survived, he’s better off. It’s the same theme I’ve heard after coaching, mentoring, and serving on boards with hundreds of CEOs worldwide — from Charles Schwab to Jensen Huang. No matter their industry, the leaders who endure never attribute their success to flawless planning and careers. They point instead to the genius and insights that came from the moments that went wrong — and how often they tried harder each time.

Setbacks as superpowers

Nvidia’s Jensen Huang told me: “Winners lose more often than losers. Winners try more things, more often, fail faster, and get smarter each time. Losers don’t harvest any of that genius.” He shared those insights with me at Stanford in 2010, during the opening of the Jen-Hsun Huang Engineering Center, when Nvidia’s market value was just $8.2 billion. Fifteen years later, in 2025, Nvidia became the first company in history to surpass a $4 trillion market capitalization — a nearly 500-fold increase.

Huang insists that the trajectory was anything but smooth. Product flops, near-misses, and cash crises nearly sank the company. But he claims that those failures weren’t detours on the way to success — they were the engine, helping them power in a better direction faster. Each stumble forced Huang’s team to rethink, redesign, and innovate at a pace that steady success never would have demanded. Nvidia has created more billionaires among its ranks than any other company. They will tell you that the setbacks became accelerants, pushing Nvidia to release new products with greater urgency, clarity, and conviction.

Why wounds matter

Even when the challenges are permanent, not temporary mistakes, a so-called disability often lights the path to fresh approaches to uniquely successful ventures. Charles Schwab is among dozens of founders and CEOs I’ve met whose personal struggles became strategic advantages. He battled dyslexia, a challenge that nearly ended his academic career before it began. Instead of hiding it, he leaned into it — finding an ironic connection with how much the average investor was suffering trying to understand Wall Street. While experts made investing obscure, Schwab recognized that it should be simple and accessible, creating the first ever online stock trading platform well before it became an everyday utility. His setback became his strategy: the very obstacle that once threatened to hold him back gave him clarity, urgency, and the conviction to reinvent how millions of Americans invest. I had the privilege of being at his side when that happened.

Boards want CEOs who can take a hit

Amazon CEO Andy Jassy won founder Jeff Bezos’ trust and commitment by echoing that truth: “Every important lesson I’ve ever had as a professional … has been a mistake that I’ve made or been a part of.” After all, he added, “We can find smart people anywhere. What we need are leaders who can take a punch and come back with a better plan.”

That ability doesn’t just restore confidence. It accelerates progress. Teams rally around leaders who admit mistakes, share lessons, and use setbacks as springboards. Boards and investors value executives who treat adversity as data for the next breakthrough.

If you want to be CEO Ready, as I share with co-author Byron Loflin in our latest book, start cultivating this mindset now:

• Harvest your failures. After any mistake, ask: What’s the tuition I just paid? What new solution does this point me toward?
• Trust your flaws. Don’t hide weaknesses. Use them to build diverse teams, simplify products, and connect authentically with others.
• Convert pain into purpose. Let obstacles sharpen your sense of urgency and highlight opportunities hidden in plain sight.

The bottom line

The greatest CEOs don’t see setbacks as interruptions to their story — they see them as the story. Every failure forces a choice: deny it, or use it. Those who endure don’t just recover; they reframe the problem, build something new, and likely move faster than the competition ever expected.

That’s why Branson urges leaders to turn wounds into wisdom, and why Huang drives his team to lose more often than losers. A scar isn’t a reminder of failure — it’s a blueprint. And the next breakthrough won’t come from your victories. The genius is hidden in the struggles you’ve endured, waiting for the courage to dig it out.

This article How the best CEOs turn setbacks into superpowers is featured on Big Think.

The COVID-19 pandemic is credited with accelerating the widespread acceptance of remote work, with proponents citing benefits like increased flexibility, greater employee satisfaction, and cost savings. But when Jack Nilles first proposed the idea of “telework” in the early 1970s, the University of Southern California researcher had another goal in mind: reducing traffic congestion.

It was a phenomenon that Nilles, a U.S. Air Force veteran turned NASA consultant, dubbed the “telecommunications-transportation tradeoff.” Viewing remote work as a potential substitute for commuting, Nilles sought to gauge telework’s effectiveness by partnering with a major national insurance company (whose name he still can’t divulge for legal reasons). A group of employees worked from local centers equipped with “minicomputers” that transferred data to the company’s mainframe. In the 1974 pilot study, Nilles concluded that this approach resulted in higher productivity and reduced turnover. If widely applied, he later wrote, telework could have a “significant impact on transportation costs and design.”

But remote work didn’t fully catch on for decades. Now, 26% of full-time, remote-capable U.S. employees telecommute, while 52% operate in a hybrid environment, according to the latest Gallup data. Yet views may be shifting again as the federal government and major corporations like Starbucks, Dell and Microsoft abandon work-from-home all together. 

More than five decades after the first documented study on telecommuting, Big Think spoke with Nilles, the “father of telework,” about its past, present and future.

Big Think: You started your career in the U.S. Air Force and then worked in the aerospace industry. What inspired you to begin studying the environmental benefits of remote work?

Jack Nilles: The job sort of got boring, so I decided to look around and see where else in the Earth-bound world we might apply some of the space technology we’ve developed. As part of this search, I came across an urban planner in Santa Barbara, California, who said, “If people can put man on the moon…why can’t you do something about traffic?” That was a eureka moment for me. I said, “We’ve got the technology, but nobody is doing anything about traffic, other than complaining.”

So I proposed a research study to my employer [The Aerospace Corporation], where I was Secretary of the Corporate Planning Committee. They asked what would be involved, and I told them they’d need lawyers and sociologists and psychologists. Their answer was, forget about it. I changed careers entirely from inventing spacecraft to inventing a different kind of future. 

I left for the University of Southern California [in 1972]. I invented a title for myself, the Director of Interdisciplinary Program Development, and worked with the different siloes and schools under a grant from the National Science Foundation. We began our research on what we called “the development of policy on the telecommunications-transportation tradeoff.” But we knew we’d have to conduct the study not with graduate students but with an actual corporation.

Big Think: How did you conduct the study and what did you learn?

Jack Nilles: We partnered with an insurance company — the name of which I’m still not allowed to divulge. Their problem was, they had a fairly large clerical staff and their turnover rate was roughly 33% annually. One of the prime reasons was that employees lived elsewhere from where the company was based in the San Fernando Valley. There was an implicit penalty for the stress and strain of getting a ride to work, driving or using mass transit, and the company had to pay its staff higher salaries. Even so, people got tired of the commute and left. 

We started our project in 1973, when I coined the words “telecommuting” and “telework,” which was easier for people to understand than “the telecommunications-transportation tradeoff.” And it was a success. Several months later, their turnover rate went to zero and productivity went up about 15%, in their terms. We calculated that, if they applied this to the rest of the company, they could probably save $3-4 million a year in reduced costs.

Big Think: How was the study received? Did you face backlash or criticism?

Jack Nilles: The insurance company said they liked [remote work], but [were] not going to do it. They said they were a non-union company, and if their employees were spread out in so many locations, the unions could organize them more easily. Some months later, I was at a conference that included a strategic planner for the AFL-CIO [American Federation of Labor and Congress of Industrial Organizations]. I was explaining telecommuting to him and our experiment. He said it was a terrible idea — if a company has their employees scattered all over the city, how will we ever get them organized? For totally opposite reasons, it was discarded by both sides.

Big Think: When did people start becoming more open to the idea of remote work?

Jack Nilles: In the early 1980s, we started a project with several Fortune 100 companies. They liked it and decided to adopt it for many of their employees. Except for IBM and AT&T, I still can’t disclose their names — we couldn’t talk about it. 

Telecommunications technology in 1973 was such that you could not set people up in their homes. Our fallback was to have them work at these [local] centers with minicomputers. When personal computers began appearing in the late 1970s, you suddenly had the potential of including your office work on your [home] computer. That didn’t start to become real until the mid-1980s. With the internet, communications became widely available, and then the problem became more about aligning with corporate culture. You could always find some way in which people could use existing technology to communicate effectively at a distance, at least some of the time, whether once a week or every day. Clearly, there was going to be some spectrum of possibilities. 

Then, somebody from the state of California, whose job was reducing the state’s need for real estate, came to me. Over the years, I devised a program with the state of California, starting around 1985, with several hundred state government employees, both in San Francisco and Sacramento. We developed a fairly intricate system of first defining the success criteria. We had to look at the legal issues involved. We trained managers on how to be managers when you can’t see your employees, and vice versa. The study was a success.

Big Think: How did you then develop your telework system? 

Jack Nilles: Instead of plunging people into it, we developed work rules and procedures — who’s responsible for what; when and how you report. We never had a problem finding potential telecommuters. People love to be able to work at home — it was much harder to convince their employers that they were actually working. Time and time again, we discovered that remote workers’ productivity increased, even with one day a week, by 10 to 20%. We later partnered with the state of Arizona [and Washington and the city of Los Angeles]. Remote work was slowly and steadily growing around the world. In the 1990s, I did a lot of work in Europe, which was even more complicated. 

But the magic event that really made people understand how it all works was COVID. It wasn’t a choice — if you wanted to survive [as a company], you had to get employees out of the office. Overnight, we had a world full of instant telecommuters. Because most organizations basically had no experience with it, I thought there would be a lot of collapses. But the technology was good enough by 2020 that they could finally figure it out for themselves. The first month or two was traumatic for many people, but they made it. And sure enough, the employees loved it — so much that their CEOs are now telling them to return to the office.

Big Think: What about the downsides of remote work?

Jack Nilles: Loneliness is something we considered from the beginning. We work on this by making sure that remote employees are indeed communicating with other people — with the rest of the world and not just their employers. People have to be careful and be aware of it. 

Our biggest issue is not people feeling left out as much as people working too hard and getting them to stop in the afternoons and evenings.

Big Think: Are environmental concerns still the mainstay of your argument in favor of work-from-home?

Jack Nilles: The environmental concerns were, of course, a major reason for doing the research. But selling to companies in the interest of making money means you have to look for other reasons — teleworkers are more productive, you can recruit them more easily and your other costs go down. The bottom line: impact. That being said, the environmental aspects are becoming more and more important with climate change.

Big Think: What do you think the future holds for remote work?

Jack Nilles: For small companies, it’s easy. For medium companies, it’s so-so. For many big companies, it’s a no, especially in the financial industries. This transition started as a result of COVID, and it will continue. We’re seeing the movement of people out of big cities into smaller towns. More people are coming into the office part time. It’s starting to feel like normal business, finally. This is actually faster than I expected acceptance to happen because of the mental inertia that it takes for people, especially once they reach management level, to change the way they look at the world.  

I’m glad, of course, that it worked. But it gives me another set of worries that maybe it worked too well. We’ve got people who work remotely, and who knows what else they get into? But generally, I’m satisfied, especially 50 years since the first study. Today, it’s not just an American phenomenon — it quickly spread to the rest of the world. The resistance is the same everywhere: managers who are scared to deal with people that they can’t see. It’s a universal phenomenon. But with experience and proper training, it works. We’ve got millions of people doing it, instead of hundreds in the 1970s and ’80s. The future looks pretty ripe for it.

This article Remote control: Meet “the father of telework” is featured on Big Think.

A couple weeks ago I wandered into a digression about toxic workplaces. Consider this week’s Nightcrawler another small detour into the forgotten value of boredom.

Last Saturday, our four-year-old didn’t sleep well. So on Sunday morning, I did what many semi-desperate parents have done for generations: I loaded her into her carseat, and set out for a long, pointless drive to get her to fall asleep.

Thankfully, the ruse worked. As we wound our way toward the Oregon coast, she nodded off after a promised donut. I reached for my headphones, ready to salvage my odyssey with a podcast or something vaguely productive. And then: disaster. I realized I’d forgotten them.

At first, I was bored. My brain, conditioned by a decade of smartphone use, kept reaching for the familiar dopamine drip of constant input. And I know I’m not unique in this. Most of us have become habitual grazers of digital noise… which is the polite way of saying we’ve become information junkies, always craving our next hit.

But about half an hour into the drive, once my initial agitation faded, my mind finally began to wander. A problem I’d been wrestling with for weeks suddenly clicked into place. I’d daydreamed myself into a resolution. Later, I laughed at the irony: doing nothing for an hour — just driving toward the coast on a foggy Sunday morning — ended up being the most productive stretch of my week. Eureka.

The bigger story, of course, is how proudly modern culture has engineered boredom into extinction. Yet boredom — and the kind of aimless mental wandering it permits — has always been one of the mind’s oldest creative engines. Boredom is not a menace: it’s a cognitive tool, and we’ve nearly erased it.

In 2014, researchers at the University of Central Lancashire found that participants forced to do a boring task (copying digits from a phone book) later demonstrated higher creativity than those who weren’t bored at all. As the authors concluded:

“Until recently, boredom has been viewed as a negative emotion with only negative outcomes, but the current study adds weight to the evidence that suggests that boredom can sometimes be a force for good. This means that it might be a worthwhile enterprise to allow or even embrace boredom in work, education and leisure.”

In other words, boredom triggers an internal restlessness that pushes the brain to generate its own ideas. There’s a saying that “an idle mind is the devil’s workshop.”

I’d invert that. An idle mind, in fact, is often where the good work begins.

Unfortunately, we’ve designed modern life to prevent precisely this state. We fill the smallest pockets of time — elevators, checkout lines, crosswalks — with input. Which is why boredom, especially now, feels almost revolutionary.

So here’s the idea I keep coming back to: if you want more genuine, original insight, stop filling every moment. Stop being productive. Get bored on purpose. Close your inbox (including this one). Skip the next podcast. Take a walk without headphones. Go for a drive and let your mind drift. As the Buddhist teacher Sylvia Boorstein once said, “Don’t just do something — sit there.”

P.S. When my daughter woke up with thirty minutes left in the drive, she groaned, “I’m bored.” Excellent.

A few more links I enjoyed:

How to be ambitious without losing your soul – via The Long Game

Key quote: “‍A former tech founder turned spiritual adventurer, Scott [Britton] hit a familiar wall: after years of chasing success, he realized that achievement alone wasn’t enough. His new book, Conscious Accomplishment, maps a middle path for people who pursue excellence, without losing awareness in the process. The book challenges a cultural assumption — that ambition and inner life must run on separate tracks. You can’t have both, we’re told: become a monk or build a company. Scott’s view is more integrated. He argues that your work, family, and even your frustrations can all become part of a daily practice.”

Beyond the Machine – via Frank Chimero

Key quote: “‍Thinking of AI as an instrument recenters the focus on practice. Instruments require a performance that relies on technique — the horn makes the sound, but how and what you blow into it matters; the drum machine keeps time and plays the samples, but what you sample and how you swing on top of it becomes your signature. In other words, instruments can surprise you with what they offer, but they are not automatic. In the end, they require a touch. You use a tool, but you play an instrument. It’s a more expansive way of doing, and the doing of it all is important, because that’s where you develop the instincts for excellence. There is no purpose to better machines if they do not also produce better humans.”

This article The brilliance of boredom is featured on Big Think.

When Davon Moseley of @royaleeats first began posting pictures of the meals he cooked during the pandemic, little did he know that he was laying the foundations for a culinary empire. Several years and hundreds of uploads later, he’s serving up soul food recipes to an audience larger than the population of his home city, Washington DC. As far as he’s concerned, though, everything he’s done up to this point is still an appetizer.

After amassing millions of followers on Instagram, TikTok, and YouTube — and landing partnerships with companies like Kraft Foods and Brunch App — Moseley has started taking his business beyond social media. In addition to teaching classes, he recently published his first physical cookbook, Let’s Get Into It: 130+ Comfort Food Recipes for Novice Cooks. He also launched his own production company, First Bite Studios, whose debut project, Borders, sees him traveling across California’s diverse natural landscape in search of recipes inspired by local ingredients.

The content creator-turned-business owner attributes much of his early success to an initial lack of knowledge about the highly competitive world he ventured into. A health and wellness coach by education, he didn’t have any prior experience in video editing or SEO optimization. Where other internet chefs tune their menus to the ever-shifting appetites of the algorithm, Moseley cooked what he wanted, when he wanted, how he wanted — and in retrospect, that’s precisely what allowed him to distinguish Royale Eats from the rest of the crowd.

Big Think caught up with Moseley to discuss what other qualities he has to thank for his success, how transforming his passion into a career has changed his mindset, and why he’d rather you didn’t call him an “influencer.”

Big Think: Nowadays most people get their recipes online, on websites and social media. In this digital age, what’s the added value of a physical cookbook?

Davon Moseley: Having a physical product — it’s just a different experience. I always compare my work to the music industry. It’s one thing to play songs on your phone, but it’s another to have a CD or vinyl to listen to. It allows you to feel more connected to the art and the artist. Also, it’s tangible: If something ever happened to the internet, it will still be there.

Big Think: There’s no shortage of articles, videos, and posts on how to become a professional content creator. What’s the right move: following your heart, or the algorithm?

Davon Moseley: There’s a balance, but it’s not 50–50. Staying true to yourself helps you connect with people, but you also have to understand the system to stand out in it. Posting on TikTok is very different from YouTube. YouTube is long-form — people expect to watch a story develop over there. On TikTok, on the other hand, you have only a couple of seconds to capture someone’s attention before they keep on scrolling.

Big Think: You’ve mentioned that you’re not a big fan of the term “influencer” and prefer “inspirer.” How do you feel about being called a “content creator”?

Davon Moseley: I have a complicated relationship with that label, too. I understand why it exists and don’t think there’s anything wrong with people identifying with it. For me, though, I don’t think of myself as a content creator so much as an artist. The reason I like to compare myself to musicians is that the process of crafting something from nothing feels similar, regardless of what it is you’re crafting.

Content creation is just one part of the job. Yes, I make content. But I also develop recipes and host cooking classes. I don’t like to confine myself by calling myself a content creator or influencer. Influencing is about getting someone to follow you. Inspiring is broader: it’s about helping someone see what’s already inside themselves. Words are powerful, and if we’re not conscious of how they’re used, we can end up boxing each other into definitions that don’t really fit who we are and what we do.

Big Think: People sometimes underestimate how much work goes into content creation. Have you ever been in a situation where you felt like your job wasn’t taken seriously? 

Davon Moseley: Yes — I’ve even experienced it with my own family. People struggle to understand my schedule or assume I have all this free time just because I work from home. But I think it ultimately comes down to me understanding what I’m doing — to knowing the truth of the matter and not being swayed by anyone else’s perception.

It’s not my job to make everyone understand my life or my business. My job is to follow through on my vision and achieve the goals I’ve set for myself. Of course it can be frustrating when people are dismissive about what you do, but I think that happens in every field. Whether you’re an entrepreneur or a politician, there’s always going to be something about your job that other people don’t fully get. 

Big Think: As someone who loves their job and derives meaning and energy from it, how do you maintain a healthy work-life balance?

Davon Moseley: My work-life balance used to be terrible because, when I started Royale Eats, there wasn’t really a blueprint for me to follow. I had to figure out everything on my own. There were days when I’d wake up and work until I went to bed. Since I had never run a business before — and didn’t go to school for entrepreneurship — I didn’t know what balance looked like. 

I’m a naturally hard worker, and being a hard worker can make it easy to neglect rest. I actually had to teach myself to take breaks, to pick up hobbies outside of work, and stick to them. Once I did all this, though, I immediately saw how much it helped me both personally and professionally. Taking time to step away from work makes me more creative, focused, and grateful when I come back to it.

Big Think: When you’re presenting or narrating a video, you don’t talk the same way you would in everyday conservation — there’s a different tone and energy. How did you find your online voice?

Davon Moseley: I’m my own biggest critic and overanalyze everything I do and say. When I started out, I wanted to talk in a way that sounded like a natural conversation, not a monologue — reading sentence after sentence. I also wanted to talk in a way that made people feel relaxed, not anxious. 

I have an accent and naturally talk fast because that’s just how people speak where I’m from and grew up. But since I can’t talk that way when I’m making content, I learned to slow down. I also make sure to avoid certain slang words so people from different parts of the world can understand. If I want someone in Germany or South Africa to follow what I’m saying, I have to be mindful of that.

Communication is one of our most important skills as humans — it’s how we connect. Different scenarios call for different approaches. The way I talk with friends is different from the way that I speak in a professional setting. It’s not that one is real and the other is not, just that there’s a time and place for everything. It’s not about what’s fake: it’s about knowing how to communicate effectively depending on the circumstances.

Big Think: What do you do when a piece of content you’re really passionate about doesn’t perform well — when your own tastes seem to differ from that of the audience?

Davon Moseley: Even though I have full control over what I do, I have a business to run. I don’t have someone giving me direct orders, but in a way, my audience is my boss. Yes, I can do whatever I want, but if the audience is not receptive, my business suffers. 

My background is in dieting and eating healthy foods — I followed a plant-based lifestyle for several years, and learned a lot of recipes that way but my audience tends to gravitate toward comfort food. When I try to make something healthy, it doesn’t always perform as well. Not because it isn’t good — it’s just not what people look for.

The food I made one or two years ago might not reflect the chef I am today. I want to evolve, but I have to do so while staying connected with my audience. Rather than taking away what they love, I gradually introduce them to new things. That way, they can see my progression and maybe feel like they’re progressing with me. 

Big Think: What was the moment when you realized that Royale Eats had developed from a passion project into an actual business?

Davon Moseley: I think it was when I started to see a community forming around what I was doing. At first, I was just sharing what I cooked that day, posting a plate here and there. But then people started asking, “Hey, can I buy that?” and “If you make that again, I’ll pay for it.” That’s when it clicked for me. When people are willing to spend their hard-earned money on what you create — that’s not just support, that’s investment. They’re saying, “I believe in what you’re doing, and I want to be part of it.” 

Big Think: When this shift took place, did your mindset change as well? Did you start to approach cooking differently when it became your primary source of income?

Davon Moseley: The more my audience grew, the more I started looking at myself critically, in a productive way. I realized that how I carry myself day-to-day directly affects my work. My energy, my mood — all of that shows up in the videos I make, in my voiceovers, even in the recipes. People can feel where you’re at, whether they realize it or not. I can usually hear it right away when I play back one of my recordings. And if my energy’s off, I’ll take a breath, reset, put myself in the headspace I want others to be in when they watch me, and do it again.

Big Think: Sometimes people who turn their hobby into a business fall out of love with that hobby. Have you experienced this at all?

Davon Moseley: When people see me cook, there’s this expectation that it has to be perfect or “content-worthy.” The kitchen used to be my escape. Now, the kitchen — any kitchen — reminds me of work. I’ve had to make a conscious effort to counter that. Sometimes, I’ll cook something and not record it, just to remind myself that I still love cooking for what it is, and not just for what it has led me to.

This article Chef’s kiss: How to upscale from side hustle to sky’s-the-limit is featured on Big Think.

That certain concerns about technology are, for lack of a better word, universal, should be evident to any historian. Early Daoists cautioned against technologies that sully the spirit and ignite chaos. The Zhuangzi [one of the foundational texts of Daoism] warns against the use of “clever machines” and suggests that they are better left alone by those walking the righteous path. This is not unlike the apprehension that many European intellectuals harbored toward the technologies of their respective times, whether that be the early widespread resistance in Western Europe to Hindu-Arab numerals, the reluctance of the Catholic Church to embrace perspectival painting and scientific method in the early Renaissance, or the anxiety that swept over the European continent during the introduction of photography, the film camera, the microscope, and the steam engine. “To try to capture fleeting images is not just an impossible undertaking, the very wish to do such a thing is blasphemous,” reads a nineteenth-century German critique of early daguerreotype technology. “Man is made in the image of God, and God’s image cannot be captured by any machine.” 

Yet admonition and derision are far from being the only reactions. Humanity’s enthusiastic embrace of technology, which is so patent in practice, can also be found in the realm of ideas. In China, Mohists took an artisan position, forcefully arguing against the Confucian ideal to transmit rather than create. The Neo-Confucian, Shen Kuo (1031–1095), whose Brush Talks from Dream Brook documents scientific innovations in physics, astrology, mathematics, and medicine, claimed that there was no contradiction between Confucius’s teachings and technology. Despite the apparent Daoist rejection of machines, there is arguably a Daoist attitude that views the emergence of technology as consistent with the Way. The ideal of wuwei (无为, effortless action) is commonly illustrated by the famous story of Cook Ding, who learned to follow the hollows and openings of an animal’s body and effortlessly wielded his knife to carve an ox. Such “effortless action” is arguably aligned with bottom-up trends in machine learning and recent discoveries in neuroscience regarding the spontaneous intelligence of nonconscious cognition.

Perhaps the most significant contribution to any “pro-machine” tendency in Chinese thought, however, comes from the profound influence of Buddhism. Buddhist culture and ideas, which spread across Asia through the trade routes and communication networks of an early urban cosmopolitanism, have long had a deep affinity with technoculture. Buddhists were amongst the first adopters of print technology, and have since then actively incorporated a wide range of “technologies of salvation” including photographs, WeChat dharmas, smartphone rituals, and robot monks. Buddhist modernity made a deep impact on thinkers like Tan Sitong who saw in the emerging world of electromagnetic communication an expression of New Confucian philosophy.

These traditions are occasionally called upon to justify why China, according to some studies, scores high on the social acceptance of robotics and low on AI fear. To explain why “China does not fear AI,” for example, Bing Song [Director of the Berggruen Institute China Center] draws upon schools of thought whose early practitioners often rebuked machines; she writes that “notions of living with other intelligent beings, most of which are more powerful and long-lasting than humans, abound in Daoist and Buddhist thinking.” Indeed, the Mahayana conception that even non-sentient beings have Buddha Nature seems to resonate with the possibility of “artificial” intelligences. 

[Philosopher] Gai Fei points out that contrary to a perceived organic prejudice amongst Daoists, practitioners of inner alchemy actively rejected a plant-based diet in favor of ingesting more durable materials such as crystals and metals. In their quest for immortality, they sought to create a metallic body, enacting a kind of proto-cyborgian becoming. Confucianism has also been called upon to accommodate artificial intelligence: contemporary philosophers such as Li Chenyang and Yao Zhongqiu argue that, rather than threatening harmonious human social order, AIs may belong within the Confucian ethic of “graded love” and be counted among the creatures (wu, 物) with which we partake in friendly and mutually beneficial relationships. Likewise, Stephan Angle proposes that the capacity to program software, which can encourage certain virtues, suggests that the ubiquity of AI may be of use in constructing a society with Confucian values. 

Yet attitudes and narratives around artificial intelligence both inside and outside of China are ambivalent and contradictory. Arguments suggesting an inherent sympathy between AI and traditional Chinese philosophy can easily serve national business interests and bolster geopolitical aspirations. A few years ago, China “plunged into an artificial intelligence fever,” as enthusiastically described by the well-known executive and investor, Kai-Fu Lee. In 2016, more than 280 million Chinese viewers tuned in to watch Google’s AlphaGo defeat Lee Sedol in the ancient game of Go. In 2018, the bookshelves behind President Xi Jinping in his New Year speech displayed two AI-related publications, Pedro Domingos’s The Master Algorithm and Brett King’s Augmented: Life in the Smart Lane, and Xinhua News Agency unveiled its new AI anchors, Qiu Hao and Xin Xiaomeng. 

There are hopes amongst both government officials and corporate leaders that AI and robotics can finally resolve the flaws of a centralized planned economy. For some, the future of the party lies, ultimately, in the realization of a “fully automated luxury communism” with Chinese characteristics. Yet, despite a 2015 government declaration that China would become a leading AI power by 2030 and the optimistic prognosis of entrepreneurs such as Lee, and even accounting for disruptive breakthroughs from China, it is still in America where the most advanced, cutting-edge research takes place, and it is still American technoculture that reigns supreme in the popular imagination.

This article The roots of AI in Chinese philosophy — and what it could mean for business is featured on Big Think.

Across every measure, from health to economic productivity to civic trust, America’s social fabric is fraying. Nearly half of U.S. adults report feeling lonely; only one in five employees say they have a close friend at work; and according to the Pew Research Center declining trust costs the economy an estimated 1–2% of GDP each year through friction and inefficiency. The U.S. Surgeon General has warned that loneliness now rivals smoking in its impact on health.

At the 2025 Volunteering Reconnected Summit in Silicon Valley — hosted by the U.S. Chamber of Connection and co-sponsored by Big Think — leaders from business, government, academia, and the social sector gathered to discuss community service, and its untapped potential to rebuild human connection at scale. A new consensus emerged.

While disconnection is systemic, so too are the solutions. Volunteering, one of humanity’s oldest social practices, may be the most scalable, evidence-based way to close the connection gap. Here are five key insights that can help organizations, communities, and leaders rebuild connection at speed and scale.

1. Reframe connection as infrastructure

America’s connection crisis is not just emotional. It is structural. Over the past century, shifts in technology, mobility, and culture have eroded the everyday interactions that once anchored civic life. While families now spend 70% less time with friends than two decades ago (American Time Use Survey, 2023), having strong and secure connections can increase someone’s life expectancy by 50% (Stanford Center on Longevity, 2023).

Connection, experts argue, should be understood not as a behavior but as infrastructure: a measurable system of trust, empathy, and belonging that underpins public health, safety, and productivity. At the summit, leaders agreed that reversing disconnection requires coordinated design across sectors, embedding connection outcomes into how companies, governments, and communities operate. And the time is now.

As Carrie Varoquiers, Chief Impact Officer at Workday observes, “Our online friends are fantastic, and AI companions might be too…but we cannot let that version of connection become the new norm if we want our children and businesses and communities to flourish.”

2. Create rituals of trust

Community service has long been a vehicle for civic good. But when deliberately designed, it also becomes one of the most reliable laboratories for trust and belonging. It transforms “days of giving” into sustaining rituals of trust. Studies show that consistent volunteering boosts happiness, trust, and empathy, while reducing stress and isolation. Yet today, only one in four Americans who want to volunteer actually do (Stanford Center on Longevity, 2023).

In cities like New York, it’s already starting to happen: Over half of NYC volunteers engaged across political lines, and 78% built trusting relationships with people holding different views — a model for what’s possible nationwide (NYC Service, Mayor’s Office, 2025).

The Volunteering Reconnected movement reframes service as more than a charitable act. It is a reciprocal process in which both giver and receiver learn, grow, and connect. “Service isn’t one-way,” notes the Volunteering Reconnected Founding Report. “It’s reciprocal. Everyone gives and receives something of value.”

3. Establish a taxonomy of “connection labels”

For decades, connection has been treated as a byproduct of service, valuable but intangible: A company measures volunteer hours logged or nonprofits served, but not the cross-team bonds strengthened, the skills developed, or the empathy built across roles. Without shared metrics, its impact remained invisible. The Summit called for a new standard — make the invisible visible.

Drawing inspiration from nutrition labels and carbon accounting, leaders proposed “connection labels”: a taxonomy that identifies the type of connection each volunteering experience fosters. For example, bonding (strengthening ties within groups), bridging (building trust across difference), or collective pride (creating shared purpose and belonging). This common vocabulary, supported by emerging frameworks and measurement tools developed by the U.S. Chamber of Connection, allows organizations to design, fund, and measure connection with the same rigor once reserved for outputs and hours.

The potential ripple effects are profound. For business leaders, connection metrics justify investment by linking social connection and trust to engagement and performance. For volunteers, labels clarify what to expect, helping them choose experiences that match their social and emotional goals. And for society, shared measurement creates comparability, transparency, and momentum, turning connection from a feeling into an outcome.

4. Replace hierarchy with reciprocity

At the heart of the Volunteering Reconnected movement lies a shift in mindset, from charity to mutuality. Traditional volunteering often positions one group as the helper and another as the helped. Volunteering Reconnected replaces this hierarchy with reciprocity: both sides bring gifts, both sides grow.

Mutuality re-centers humanity in systems increasingly driven by automation and scale. It asks organizations to design service experiences that cultivate empathy, vulnerability, and shared purpose — the same capabilities that power effective collaboration and inclusive innovation. For example, when employees or students engage in structured community projects that blend reflection, skill development, and impact, everyone learns from the exchange.

As AI takes on more routine and technical tasks, the value of distinctly human skills is rising. McKinsey estimates that demand for social and emotional skills will rise by up to 25% this decade, and Deloitte finds that 92% of executives now rank empathy and collaboration as critical leadership competencies. Volunteering becomes a real-world practice ground — a “living classroom” — for these skills. As Emily Rasmussen of Grapevine noted, “When we serve together, we’re not just helping others, we’re building the muscles of connection that make teams and societies thrive.”

5. Clock up “connection hours”

The movement to reconnect America is about intentionality and integration. To move quickly and gain traction, connection-first design must integrate seamlessly into existing volunteer systems, corporate platforms, and civic frameworks. This will require new infrastructure: shared standards, funding models, and policies that recognize connection as a measurable social good. Much as “climate” evolved from the broader concept of “environment” into a defined cause area, “connection” is now emerging as a public good vital to health, democracy, and prosperity.

Through pilot partnerships, organizations across sectors are beginning to recognize “connection hours” as visible, reportable acts that strengthen belonging and trust. They’re elevating everyday activities that have always fostered collaboration and community — planning block parties, leading walking groups, organizing social clubs — but have rarely been acknowledged or formally supported as volunteering within corporate or civic systems. The Volunteering Reconnected Founding Report outlines how this framework is taking shape across the country and offers practical guidance for organizations ready to join the movement.

The U.S. Chamber of Connection’s certification as the nonprofit of record and leading authority validates and counts civic acts of connection across volunteer platforms. It also provides marketing, toolkits, and training to help organizations expand their impact.

A new civic infrastructure

The Six Points of Connection offer a roadmap for rebuilding connection and trust, and Volunteering Reconnected provides the blueprint for doing so with speed and scale, through service. It treats connection as both a social and economic asset that can be measured, designed, and strengthened through collective action.

“While loneliness has the potential to kill,” Surgeon General Vivek Murthy reminds us, “connection has even more potential to heal.”

That healing will depend not on algorithms, initiatives, or policies alone, but on what the Summit called shared purpose in motion: millions of people and organizations across America moving together to make connection the defining cause of our time.

As we approach America’s 250th year, the state of our social fabric stands as both a warning and an opportunity. We all  have a choice. We can accept the erosion of connection and trust — or commit to rebuilding it through coordinated innovation and civic will.

This article 5 ways to rebuild human connection at speed and scale is featured on Big Think.

There are moments in our lives when progress cannot be forced. No strategy will suffice, no clarity will emerge on demand. We feel clueless about how to influence the behaviors of a group of people with whom we interact; our colleagues, the market, our family, our friends.

We have no idea what happened within our organization, or within the ecosystem of our community. But everything around us seems to be in flux. Confusing. Unsteady. Separated from any sense of equilibrium.

We’ve tried everything we know to make changes. We’ve developed positive habits: eating less, exercising more, giving ourselves more downtime, being patient with our parents, more understanding with our partners. But nothing we do seems to affect the situation. We cannot find our way forward. If anything, we are regressing backward. And we’re not sure why.

These moments are not the interruptions to the journey. They are the journey.

[Priest and writer] Richard Rohr names this clearly: “Some kind of falling, what I call ‘necessary suffering,’ is programmed into the journey. It is needed to teach us that we are not in control, and it is never what we expected.”

I was half way through writing this article when I got the call to say that my father had just died. The following weeks became quite an experience of ‘necessary suffering’. Dealing with the details. Navigating the challenges. Occasionally getting a few moments to start to process the last 43 years of relationship with my father.

Grief has crept up on me quite a few times already. No amount of preparation can substitute the shock of the moment, the body’s visceral response to loss. To a very common, yet very challenging, experience of necessary suffering and loss.

This suffering, if we let it, can become alchemical. It burns away our illusions of control. It softens our certainty. It brings us to our knees.

Initially, this often feels like defeat. But over time, as the strong emotions subside, and the urges to run away or fix or break things settle down, our sense of pain and defeat metamorphoses into openness. Acceptance. A realization of the futility of fighting.

We become curious as to ‘what else’ is life asking of me? How else can I experience this grief, this sense of having some of my power and control removed from me.

This liminal space is where wisdom takes root.

The liminal space we’d rather skip

Much of modern leadership is designed to escape this space. We manage; we move on; we become efficient. But in the deeper current of leadership, we learn to stay with what is unresolved.

In this liminal space, we’ve left behind certain ways of living and leading, but we’re not yet sure what new ways are emerging. What other path to take. We are on the threshold of something new, yet still in this ‘in between space’.

Staying here takes courage. Here, we don’t get the answers. We get silence. We get unravelling. But if we stay long enough, we start to feel the stirrings of something else: not certainty, but trust. Not clarity, but rootedness.

And then the slow reassembly begins — not of our former self, but of someone deeper, truer, less defended. At the end of his wonderful book, Anam Chara, John O’Donohue reminds us of how necessary suffering is built in to every part of the human experience, right from the very beginning:

“Though we all came through it, the journey of birth is so forgotten, so taken for granted. Yet for the child, birth is a dark, forlorn and terrifying journey. The child leaves the only world it has known and crosses out into the unknown, alone and in terror. It is a journey that involves loss, separation and a frightening sense of falling. The child has no idea that its painful journey is about to lead into a new world, a world full of light, color and the faces of others. From the child’s perspective, the journey through the birth canal must seem like death. Yet, it is the necessary journey into life.”

The wound that opens the door

Every leader who has truly grown in depth can point to a wound. A loss. A collapse. And every true elder I know speaks of that season not as the end of their leadership, but as its beginning.

We cannot schedule transformation. But we can make ourselves available to it. Here are a few invitations that help hold space for this sacred undoing:

1. Name what is dying. Ask: What roles, stories, or identities are falling away? Let them go with dignity.
2. Welcome the cocoon. Engage in daily moments of stepping back — breath, silence, stillness — these can help us to stop resisting the descent. Let the discomfort of the in-between be your teacher, not your enemy.
3. Seek to be seen, not solved. Find situations where your pain doesn’t have to be explained — only seen by others. These can be in relationship with family, friends, colleagues, coaches, therapists or other helping professionals. In community, we remember: you are not alone here.
4. Mark what is emerging. Create small rituals to acknowledge change — a walk in silence, carrying a stone that represents a ‘necessary suffering’ until you feel ready to let it go, write a simple journal entry that begins: “Today, I noticed…”

A leader formed through fire

Embracing necessary suffering isn’t about romanticising suffering. Pain is real. Loss is hard. But if we are willing to meet it with open eyes, it becomes more than an affliction. ‘Sit with the pain until it heals you’ is an old adage that a friend of mine shared with me years ago. It’s the only healing path that I’ve experienced myself.

A leader who has faced their own breaking is a leader who can hold space for others to break and not rush them toward a fix. They are less reactive, more patient. Less certain, more present.

And in a world of fast answers and polished masks, this kind of leadership is radical.

This article How to lead from the liminal space where wisdom takes root is featured on Big Think.