$52 for two coffees and a bottled water at LAX. A cheesesteak for thirty-five a few days later in Philadelphia. Vegas was no better.

I am aware that airports are their own economy, and that a handful of concessionaires hold monopoly leases, selling to people who cannot walk down the block to a competitor. I know that, and on its own it does not prove much about inflation.

But it is what first sent me looking. The discrepancy between what I read in the news and what I am feeling in everyday life was not making sense to me. I wanted to know whether the feeling pointed to something real, or whether I was reading too much into a run of expensive airports.

Two numbers, one word

Officially, US consumer prices rose about 3.4% through July 2026, down sharply from the 9.1% peak of June 2022, itself the highest reading since 1981. Compare today with where things stood in January 2021, and the price level – meaning what the basket costs in dollars rather than how fast that cost is rising – is nearly 27% higher. The headline number only tells you how fast prices are rising now, not how far they have already traveled over recent years.

It took me a while to separate the two. Inflation is the speed. The price level is the distance. Or put another way, inflation is the speedometer. The price level is the odometer. When inflation falls from 9% to 3%, the car has slowed down. It has not reversed, and the odometer does not run backward.

Some categories have moved much further than the average. Since January 2021, ground coffee has roughly doubled; ground beef is up 74%, and gasoline is up 67%. These are single items rather than the index, and they moved further than most things did. They are also what was in my hand at that airport, and in most of the receipts since then.

The whole decade before the pandemic averaged about 1.8% inflation. Today, we treat 3.4% as a win, nearly double what we used to call normal.

Accumulation

Each year gets reported on its own, and each one sounds like progress. About 5% in 2021, 8% in 2022, 4% in 2023, then roughly 3% in the years since. Told that way, it is a problem being addressed. Told the other way, it is a bill that keeps growing. A basket that cost $100 in January 2021 costs about $127 today – but is that even the same basket?

Nothing gives it back. The only thing that brings the price level itself back down is deflation: prices actually falling, or currency devaluation. Lower inflation simply means they rise more slowly, which sounds appealing until you realize it is the outcome central banks work hardest to prevent, because when people expect things to be cheaper next month, they stop buying them this month.

Which means the 2% everyone treats as the definition of stability is a target for the rate, not the level. It is a commitment to prices rising forever, gently enough that we stop noticing. At 2% a year, they double every thirty-five years. By design.

Substitution – are we even looking at the same basket?

The deeper I went, the stranger it looked. The official figure is not measuring the price of buying the same things you bought before. Since 1999, most of the CPI has been calculated with a formula that allows for a modest amount of consumer substitution. As one item’s price jumps, the methodology assumes shoppers partly shift to a cheaper alternative, which smooths out the felt pain of any single spike.

In practice, that means if beef gets expensive and you buy chicken, the index partly records the chicken. You ate. You just did not eat what you wanted, yet the statistic treats that as broadly the same outcome.

There is a real logic to it: the index is trying to measure what it costs to maintain a standard of living, not what it costs to buy an identical basket twice. But “standard of living” is doing enormous work in that sentence. It is the difference between the cost of keeping going and the cost of keeping what you had, and most of us are trying, at least, to live in the second one.

The same move shows up in the biggest expense most people have. CPI does not use house prices or mortgage payments to measure the cost of owning a home. It uses an estimate of the rental value of that home instead. Economically coherent, and nothing like the number facing someone trying to buy.

None of these choices is a conspiracy. Each is defensible on its own terms. What struck me is that they all lean the same way. Each one smooths things out to a substituted life at a higher cost.

The house

Take the biggest purchase most people ever make. No cheaper version of a house counts as the same thing, and no average softens it.

In 1985, the median American home sold for $82,800 against a median household income of $23,620, about three and a half times what a family earned in a year. Today the median home sells for roughly $409,000, against a median household income of about $84,000. Close to five times.

Incomes went up about three and a half times over those four decades. Houses went up nearly five. You could buy a smaller house, move somewhere cheaper, or rent instead, but that changes what your income buys you. No single annual figure captures that shift. The same story applies to the next-largest purchase most people make, usually a car, and on down the list. That, I think, is the number people mean when they say things feel unaffordable.

Back at the gate

I still do not know whether $52 for two coffees and a bottle of water was unreasonable or simply what a captive market charges. Probably both. But the airport was the least of it. It was the assumption that a single number, 3.4%, calm, declining, nearly normal, was supposed to explain how that receipt felt in my hand.

That is not what it measures. It is measuring the speed of a car whose odometer nobody reads, using a basket that assumes you will eat chicken instead of beef, and treating the roof over your head as a rental estimate. Every one of those choices is defensible. None of them is what “3.4%” sounds like it is telling you.

These are the questions a single figure was never built to answer. What would an honest number look like, not the speed, but the distance, in the goods you actually buy? What would it cost, today, to buy back the life you had ten years ago, rather than the adjusted version of it you have been sold instead? And should a figure built around prices doubling every thirty-five years really be the one we use to reassure ourselves that everything is fine?

I do not have all the answers. But I know this much: it is not just in my head. Inflation at 3.4% is not a lie, and coffee at double is not an illusion. They are two honest measurements of two different things. We have spent years arguing about the economy using only one of them.

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“The past resembles the future more than one drop of water resembles another.”

— Ibn Khaldun, the Muqaddimah, 1377

Time has Never Been Linear

Every autumn, without fail, the trees let go of their leaves. To the human eye, it looks like loss. But nature knows better: the shedding is simply preparation. A few months later, without fanfare, spring arrives, the buds return, and the birds migrate home. Without exception. Because despite our perception that time is linear, time, it has been argued for millennia, is circular. Not only are days cyclical, repeating the hours, but so are the years, repeating the months and seasons. And ultimately, so are generations and saeculum.

Across centuries and spanning Empires, there was agreement in the circularity of time. The Greeks wrote of successive ages of the world and of a Great Year in which the cosmos returns to its beginning. Indian philosophy imagined the yugas, vast cycles in which the world decays and is reborn. Classical Chinese thought watched dynasties rise under the Mandate of Heaven and fall through their own complacency. And six hundred years ago, the Arab historian Ibn Khaldun described how dynasties are built by the hardened and the hungry, then dissolved four generations on by heirs who have only ever known comfort. None of these traditions believed that history repeated itself exactly. They did, however, believe that human nature did.

Before continuing with these thoughts, I should first share a disclaimer: I am an optimist by temperament and an investor by profession, and searching for opportunities in uncertain terrain is how I spend my days. When the world began to feel like it was coming to an end a few years ago, I went searching for a better way to see it. I discovered that whereas we have come to believe that time moves in a straight line and that civilization marches from primitive to advanced, for millennia – for most of human history - people believed the opposite. So I dug deeper.

The Saeculum

The idea that time is cyclical has resurfaced in modern form almost three decades ago in The Fourth Turning, and again in 2023 in The Fourth Turning Is Here, where the demographers Neil Howe and (the late) William Strauss argue that societies move through recurring saecula, from the Latin saeculum, roughly the span of one long human life, approximately eighty to a hundred years. They argue that each saeculum contains four turnings of about twenty years, similar to the seasons of a year:

• Spring, when institutions are strong and the mood is collective and confident.

• Summer, when a new generation tears at that order in the name of the self.

• Autumn, when institutions weaken and the common fabric frays.

• Winter, a crisis, when the old order is torn down and built again around a threat too large to ignore. This, they argue, is where we are today.

The framework is influential among investors and entrepreneurs, and it is often contested. Historians rightly note that history rarely conforms to any single model, and it is best read as an interpretive lens rather than a scientific law. However, it cannot be ignored as this idea has strong value, which lies less in predicting events than in recognizing the recurring patterns of human behaviour. And that insight is deceptively simple: history repeats its conditions more faithfully than its events. Our tools might change, but our incentives scarcely do. Furthermore, it remains uncanny how we each believe our own moment is unprecedented.

Winters and Their Surviving Infrastructures

Working from this thesis, today, we are living through a winter. Previous winters, in hindsight, are all relatively obvious and the similarities that drove them exhibit similarities with today’s global situation that are difficult to ignore.

The last winter ended with the world wars, and some of the foundations driving today’s crisis can be traced to the solutions drafted then. Specifically, the foundation laid deliberately to solve the depth of the post-war crisis and create postwar financial order was drafted at Bretton Woods in 1944, while the war it would outlast was still being fought. Yet even before that, in reviewing human psychology, we can observe the railway mania of the 1840s, a time when share prices doubled, then collapsed and ruined countless once-comfortable households, yet left behind thousands of miles of track that became one of the world’s great rail networks. Similarly, earlier in this winter, the dot-com and telecom crash of 2000 to 2002 destroyed trillions in market value and left behind a glut of fiber-optic cable, most of it never even lit, that went on to carry the broadband internet. Whereas our tools evolve, our psychology scarcely does.

More recently, in 2021, roughly three-quarters of venture dollars still went into the ordinary work of the real economy, finance, health, education, food; by the first half of 2026 that share had fallen below a fifth, with 77 percent of global innovation capital now chasing AI, and in the United States barely eighteen cents of every venture dollar left for everything else. But revolutionary technology and speculative excess tend to travel together, and the object of the mania is rarely its lasting legacy. Whereas crises destroy the old systems, they also build the infrastructure of the next. Capital has crowded into artificial intelligence, and that may prove entirely rational. Perhaps the enduring inheritance of the AI boom will be something standing beside it: abundant energy, autonomous industrial systems, computing so cheap we stop noticing it.

Politically, another winter trend is repeating: trust in institutions has fallen across much of the developed world, traditional parties are fragmenting, and confidence in governments, media, and universities has steadily eroded, the legitimacy crisis Howe associates with the closing years of a saeculum.

Socially, the cycle feels just as familiar. We have made hyper-individualism a defining feature of the age; our algorithms optimize for identity over community, shared narratives splinter into personal ones, and we have never had more ways to connect and yet reported more loneliness. The platforms are new, but the questions are ancient. How much freedom can we sustain before we begin to crave order again? Community deserves an article of its own, and it is one I will come back to; but for now, it is enough to notice how far the pendulum has swung toward the self.

This is why Howe and Strauss call the study of history an exercise in humility and argue that we consistently forget why earlier generations built institutions. The generation that survives a terrible crisis builds systems to prevent the next crisis from happening. Their children inherit the stability but not the difficult memories. Their grandchildren begin dismantling what they no longer understand, until another crisis teaches us the same lesson again. We are undone less by history than by the forgetting of it. Which may be why the most dangerous phrase in any age is, “this time is different.” Sometimes it is, but usually only in the details, not the lessons.

Combined, the institutional distrust, political fragmentation, AI capital concentration, loneliness and hyper-individualism can be ready not simply as unrelated crises but as symptoms of the closing phase of a saeculum. The Fourth Turning framework has given me a different lens: what feels unprecedented may actually be a recognizable phase of social transition.

And Soon, Spring

The mistake we tend to make is believing that the Fourth Turning itself is the story. It is not. It is only one more season in a much longer repeatable history, and the story is the saeculum itself. Howe argues that the current crisis began with the financial collapse of 2008 and is now moving through its decisive years, with a resolution likely in the early 2030s. Every saeculum before it has ended the same way: with the birth of a new order rather than the survival of the old. The American Revolution, the Civil War, and the Great Depression and Second World War each gave way, respectively, to a new republic, a redefined Union, and the institutions that organized the world for the next hundred years. And whereas much of the literature today is US focused, the same pattern is true of other civilizations around the world. Each crisis felt like the end to those living it, but each became the ground on which the following eighty years were built.

So the question, for me, is no longer if we are in a crisis, or how this crisis will end. It is “what will spring look like”? Will AI merely make us more productive, or force us to redefine what only a human can contribute? Will institutional distrust deepen, or eventually give rise to institutions worth trusting again? Will we keep measuring prosperity by efficiency alone, or begin to measure it by resilience, belonging, and health?

What matters in the next saeculum is being defined today. Previous winters didn’t restore the world that existed before them – they each produced a new order. If we are indeed approaching another such transition, then the institutions, companies, political structures and definitions of prosperity that govern the next hundred years are being created right now. We are not merely living through the end of an era – we have the unusual privilege and responsibility of building the new one.

So given the cyclicality of time, and as we live through this winter together, what seeds are you sowing for Spring? I even wonder, in my optimistic nature, might the Spring bring the resurgence of broader societal consciousness?

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For thousands of years, mountains have occupied a sacred place in human history. Philosophers sought them, pilgrims climbed them, monks retreated to them, and poets wrote about them. Something about high places strips life back to its essentials. They remove distraction, demand presence, and tell you truths that are hard to hear anywhere else.

Mountains have been a constant in my own life, somewhere I return to whenever I can. Over the past few weeks, I climbed some extraordinary ones across Switzerland, and somewhere between the glaciers and the ridges and the summits, I realized I had not only been learning how to climb. I had been learning how to live, and, it turned out, how to lead.

Now that I am back on lower ground, it is not the summits I remember most. It is the sentences my guides spoke, almost in passing. Each one sounded like practical climbing advice. Each one turned out to be something much bigger.

Lesson #1: “There are many paths up. Choose the one you want.”

On Riffelberg, my guide’s patience was starting to wear thin. For what felt like the hundredth time, I had asked him which way to go: which rock, which foothold, which handhold.

“We’re going up,” he replied. “The destination is obvious. The route and steps are yours to choose.”

It annoyed me. Surely there was a right way, surely he knew it. And yet, I paused. He was not standing where I stood. He did not have my height, my reach, or my balance on those particular rocks, and the line that worked for him might not hold me. So reluctantly and cautiously, I picked my own way up, slowly, and it held.

The small exchange stayed with me long after we descended. How often do we look for someone else to validate our choices – for the next step? We ask for certainty when what we really seek is permission or encouragement. But nobody occupies our exact position in life. Nobody carries our experiences, responsibilities, fears or ambitions. Nobody has our exact viewpoint and reach for the next steps and holds. Advice can illuminate guide the way, but only we can choose the route.

I see it constantly in the founders I work with, hunting for playbooks, measuring themselves against competitors, asking investors for the “right” strategy, when every company begins somewhere different, with its own people and markets and constraints. The summit may be shared. The path almost never is. The best leaders I know do not copy someone else’s climb. They find the route that fits the mountain beneath their own feet.

Lesson #2: “Take the step you can see. The path will appear.”

The Matterhorn was relentless.

There were long stretches where I genuinely could not see how we would keep going. I would scan the rock above me for a way through, find nothing, and feel the doubt rising in my chest.

Then my guide’s voice came booming down, almost impatient: “Just take the step you can see.” Reluctantly, and not at all convinced, I did. And the moment I shifted my weight onto it, another hold appeared. Then another. The mountain had not changed at all. My perspective had.

I realized how much of life is spent trying to solve problems that belong to a future version of ourselves. We postpone decisions because we cannot yet see the entire journey. We wait for certainty before acting, unaware that certainty is often the reward for movement, not its prerequisite.

Founders feel this acutely. Everyone wants to know where the company will be in five years, everyone wants a roadmap, and yet almost every company is built one decision at a time, each step only visible once the one before it has been taken. Movement is what creates the information. Action is what produces the clarity that no amount of planning ever will.

Lesson #3: “I’ll tell you the weather when we get there.”

The forecast had promised a pleasant twenty-two degrees. Instead, we climbed through seven degrees with fierce wind, frozen fingers and cheeks stinging with cold.

I was miserable. I hate the cold. And I had remembered repeatedly asking the guide what the weather would be. Each time, he smiled.

“I’ll tell you when we get there.”

I took it, at first, for mountain superstition. It was only later that I understood it as humility. For all their decades on the rock, all their instruments and forecasts, my guides refused to pretend they knew something they did not. There is a quiet confidence in that, the kind that does not need to perform certainty. It made me notice how much of my own life runs on the opposite instinct: calendars, travel, meetings, investments, family logistics - all of it built on the belief that the future can be arranged into something predictable. But the moments that matter most tend to reveal themselves only once you are standing inside them.

Business demands the same humility. Strategies are designed in boardrooms, but markets do not read strategy documents. Teams evolve. Customers behave unexpectedly. Competitors emerge. Macroeconomic conditions shift. The best leaders are rarely those who make perfect predictions - they are the ones who notice the weather changing and let go of yesterday’s plan without mourning it.

Lesson #4: “Don’t forget to bring up the Friends.”

On Stockhorn, my guide called down: “Don’t forget to bring up the Friends!” I stopped and looked around, confused. There was no one there but us two. It took me a moment. Friends, it turns out, are pieces of climbing gear: small spring-loaded devices you wedge into cracks in the rock. As the person ahead climbs, they place them along the way and clip the rope through, and those little anchors are what hold you if you fall. You are, quite literally, held up by the Friends. I laughed at the mix-up. And then I sat with the other meaning of the word.

Success has a way of turning into a solitary pursuit. We chase - the promotion, the summit, the exit - so single-mindedly that we lose sight of the people who made the climb worth doing in the first place. I have become more and more sure that achievement means very little if you cannot share it with people you trust, admire, and actually want to be around. When I look back, the moments that stay with me are almost never the solo victories. They are the shared ones.

It holds in the work too. We measure companies in valuations and revenue and returns, but the ones that last are built on relationships, by founders who lift the people around them rather than climb over them. Maybe bringing your friends up the mountain, both kinds, is the truest measure of success there is.

Lesson #5: “Keep moving. Don’t stop.”

On Allalinhorn, hours into the climb and legs shaking, I finally said it out loud: “I can’t.” My guide did not break stride.

“You can slow down,” he said. “But don’t stop.”

It sounds like a small distinction. It is not. Stopping opens a negotiation with yourself, and it is one you tend to lose. The momentum goes, the doubt moves in, and starting again costs far more than it would have to simply keep shuffling forward.

That one landed somewhere personal. There are seasons when energy is everywhere, and seasons when putting one foot in front of the other is the whole of the achievement. In the second kind, progress is not about pace. It is about not stopping. Some days resilience is not dramatic at all. It just means refusing to stand still. Companies live the same rhythm. Growth is never a straight line; markets contract, funding thins, teams grow tired. The ones that come through are rarely the fastest. They are the ones that keep moving, however slowly, while everything around them is uncertain. Given enough time, consistency outlasts intensity.

Lesson #6: Fake summits exist for a reason.

The Eiger has a way of lying to you.

What looks like the summit is often just another ridge, with more mountain hidden behind it. Shattered, and relieved to be done with the climb, by the time I hauled myself up to what I was certain was the top, I found there was still a long way to go.

The rest did not seem that far. I was certain I would eventually get there. But, I wondered if, had I seen the real summit from the bottom, I would have pursued the climb at all. That, I was sure, would have looked like too much at that time.

Life seems to work like that, revealing itself in stages, holding back the full picture until we are ready to carry it. We are handed horizons we can actually reach, each one stretching us a little before the next comes into view. Founders live inside this without naming it. Success always looks as though it sits just past the next round, the next launch, the next market, and every milestone turns out to hide another climb. Those false summits are not broken promises. They are a kind of mercy. They keep our ambition alive while our capability catches up to it.

Lesson #7: “Look only at your path.”

On the way down that same ridge, exposed on both sides, I heard someone behind me mention the drop: a thousand meters on one side, five hundred on the other. My stomach turned.

My guide, calmly instructed, “Don’t look down.” He told me to put my eyes on the next few meters of rock and nothing else. The drops had been there for thousands of years and would stay exactly where they were. Refusing to look at them did not make them any less real, but it stopped the fear from swelling to fill space it had not earned.

I have turned that over many times since, how often distraction costs us more than the difficulty itself. Comparison, headlines, the endless scroll, the futures we imagine and dread, all of it pulls our attention off the one thing we can actually act on, which is the step in front of us. Leadership asks for the same discipline. Every company has its drops: competitors, volatility, noise. The steadiest leaders are not the ones who pretend the danger is not there. They see it clearly, name it, and then bring their eyes back to the next deliberate step.

Lesson #8: “Trust your feet.”

There was one sentence, though, that came back more than any other. A dozen times a day, on every mountain and every ridge, I heard it: “Trust your feet.” I assumed at first it was a technical instruction, about friction and balance and where to place my weight. By the end, I heard it differently.

Our instinct is to trust the data, the experts, the forecasts, the algorithms, the opinions of everyone around us. All of it has its place. But there is always a moment - on a mountain, in a boardroom, in a life - when none of it can make the decision for you. In the end, only you can shift your weight onto the next foothold.

That might be the thing I have carried furthest down the mountain. Confidence is not the absence of uncertainty; it is the willingness to move despite it. Leadership is no different. We gather the information, take the advice, argue through every scenario, and then a moment comes when the analysis runs out and judgment begins. The decisions that matter most are rarely made because we are sure they are right. They are made because we trust that, whatever happens next, we will find our footing.

The mountains gave me a great deal over those few weeks: lessons about movement, resilience, perspective, humility, and friendship. But in the end I think they were all the same lesson, worn into different shapes.

Trust your feet. Trust yourself.

The summit is rarely won by the strongest climber. More often, it belongs to the one who keeps taking the next step.

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It was the toughest and darkest of times. Mass unemployment. Deep poverty. Devastated agriculture and food insecurity. Bank failures. The 1930s. An era defined by the Great Depression. But whereas Franklin D. Roosevelt’s New Deal introduced programs and reforms aimed at relief and recovery, a more interesting idea and metric emerged from the economist Simon Kuznets: GDP.

The GDP Warning Label

Strangely enough, the man who created it tried to warn us not to use it too much. Handing it to Congress in 1934, Simon Kuznets attached a caution we would later peel off like a label.

“The welfare of a nation,” he wrote, “can scarcely be inferred from a measurement of national income.”

Kuznets had developed a metric to give a Depression-era America a way to measure its economy, and soon after, to arm and feed a war. It answered one question: how much can a country physically produce? Steel, grain, machinery, tanks. He built a ruler for output and said, plainly, that it could not measure a life. We kept the ruler. We ignored the warning.

Ninety years later, we still measure and reward GDP. Not annually. Quarterly. Once a quarter, a single number is published, and much of the world arranges itself around it. GDP. When it climbs, the country is winning. When it slips, something has gone wrong. Governments are judged by it, elections turn on it, markets swing with it. We have made it the closest thing we have to a scoreboard for a civilization and livelihood. And lately I have started asking questions that sound almost naive: what is it keeping score of today? Why do we measure it? Why do we even care?

The longer I consider GDP, the stranger it looks. A traffic jam burns fuel and increases GDP. A flood, and the years of rebuilding it forces, increases GDP. A divorce, a lawsuit, a hospital stay: all of it counts as growth. Meanwhile, the things that actually hold a life together barely register. Oxfam once tried to price the unpaid work of care, the raising of children and the tending of aging parents, at roughly $10.8 trillion a year, more than three times the global tech industry, recorded in the accounts as zero, because no invoice ever changes hands.

The opposite is also true.

The value of Google, as an example of a company that has changed lives across the world with its free Search, Gmail and Maps, is captured in the US GDP in the form of value in terms of income – through advertising, subscriptions, hardware sales, and other top-line items. The added value of its mass-consumer utility worldwide remains uncaptured. But it has, arguably, changed billions of lives.

What gets measured gets managed. Measuring GDP means that output is the goal. And somewhere along the way we agreed to call it progress. Ignoring the warning label.

Simon Was Right: Money Cannot Buy Happiness

Money cannot buy happiness, and it seems Simon Kuznets knew that – providing his warning label on measuring GDP. Output should not be the goal – life and happiness should. Instead, in our pursuit of GDP growth, we have assumed that a richer country must be a better place to be alive. I wanted to see how far it held, so I put two lists side by side.

On one, the thirty countries with the highest GDP per capita, adjusted for what money actually buys and measurable output per person. On the other, the thirty happiest countries in the 2026 World Happiness Report. Nineteen appear on both, and that overlap matters: it says, plainly, that most of the time, money helps. But in other instances, it doesn’t.

Up to a point, a richer country can be a better place to live. It is the eleven that do not overlap where the picture turns, where more wealth no longer buys more life, and where happiness is not driven by output.

The happiest countries that never reach the money list tell the first half of the story. Costa Rica is the highest-ranked Latin American country in the report’s history, sitting at fourth, and it is not among the thirty richest.

Finland has been the happiest country on earth for nine years running. It is happier than Singapore, Ireland, and the US, each of which produces far more per person.

These places are not poor; most are comfortably well off. They have simply crossed the line past which another dollar barely registers, and what they have on the far side of it is not more money.

These places are not poor; most are comfortably well off. They have simply crossed the line past which another dollar barely registers, and what they have on the far side of it is not more money.

The same idea shows up from the other direction in the richest places that never reach the happiness list. Singapore is the second-richest country on earth by this measure, and also one of the most emotionally stressed, with wellbeing slipping even as incomes climb.

The World Happiness Report’s own model explains why: past a decent standard of living, what raises a country’s happiness is community, social support, freedom, health, generosity, and trust, none of which a larger GDP automatically buys nor measures.

None of this makes money the villain. The top of the happiness list is still wealthy, stable democracies, and anyone who has seen real poverty knows income matters. The point is narrower, and stranger: the relationship is real at first, and then it runs out. Below a comfortable standard of living, more GDP can buy a great deal of wellbeing. Above it, it buys almost none.

So What (or Where) is A Life Well-Lived? Are There Better Measures?

Without being on the rich list, many countries are very happy. Community, neighbors, and clean food cannot be measured. The longest-lived people on earth cluster in a handful of places researchers call Blue Zones: Okinawa in Japan, Sardinia, Ikaria in Greece, and Nicoya in Costa Rica, the same country that ranks fourth for happiness. None of them are rich; several are among the poorer regions of their countries, Okinawa being Japan’s lowest-income prefecture. What they share is not income. It is the basics of life and being human: food grown and eaten together, close family and community, movement built into an ordinary day, and the feeling of being needed. They live longer, and by most accounts better, on a fraction of the wealth (or GDP) others chase.

Better measures exist. The UN has published a Human Development Index since 1990 that weighs health and education beside income, and there are others. They are imperfect, and most still try to press a whole life into one figure, but they exist, and they sit largely unused. Bhutan has measured Gross National Happiness for decades. New Zealand built a wellbeing budget around health, community, and the environment rather than growth alone. The UAE appointed a minister for happiness in 2016 and began treating wellbeing as policy, and Saudi Arabia wrote a Quality of Life program into Vision 2030.

None of it is a finished answer, but the instinct is right: we should be managing for the lives people are living, not their output.

Ninety Years Later

GDP can tell us, to the decimal, how much a country produces. It has never told us whether the people inside it are healthy, whether their children are learning, whether they can rely on one another, or whether the growth we celebrate ever reached their lives at all. Does the average person in a wealthy, joyless country feel anything when the GDP figure ticks up half a point?

Ninety years on, in a world very different from the one of the Great Depression, we remain fluent in what we produce and silent about whether we are living well. Will that also be what matters for the next ninety?

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Two monks, Tanzan and Ekido, were traveling together down a muddy road after heavy rain.

As they came to a river crossing, they found a beautiful young woman dressed in an elegant silk kimono. She was unable to cross without ruining her clothes.

Without hesitation, Tanzan picked her up, carried her across the river, gently set her down on the other side, and the two monks continued on their journey.

Hours passed in silence.

Finally, Ekido could no longer contain himself.

“We monks are not supposed to go near women,” he said. “Especially not young and beautiful ones. Why did you do that?”

Tanzan smiled and replied:

“I put the woman down hours ago. Why are you still carrying her?”

I love this story, and it is often one I come back to often in my mind and life – as a reminder to put things down. It is often a reminder to ask myself: What am I still carrying that I could have put down long ago?

The timelessness of this story is likely because we all find ourselves in this position at times. As a result, it is a parable frequently used in mindfulness, and leadership because it illustrates how much mental energy we waste replaying conversations, holding grudges, or worrying about things that are already over.

In life, we often carry judgments, resentments, embarrassment, or anger long after an event has passed. This creates a further burden that is rarely the event itself – it is our attachment to it. Wisdom lies in responding appropriately in the moment, then letting go.

More recently, this parable has taken on a new resonance in light of modern psychology and some of my recent reading.

For a long time, many people believed that healing meant revisiting painful experiences over and over. We replayed conversations, analyzed childhood events repeatedly, and assumed that if we just thought about our trauma enough, we’d eventually understand it and move on.

But much of contemporary psychology draws an important distinction between processing and ruminating.

Processing is an active, constructive process. It helps the brain integrate what happened into a coherent narrative. It often involves naming emotions, making meaning, feeling them safely, and allowing the nervous system to recognize that the threat is over. After processing, the emotional charge usually decreases.

Rumination, by contrast, is passive repetition, the mind circling the same thoughts without ever arriving anywhere. The psychologist Susan Nolen-Hoeksema spent much of her career mapping this second pattern, and her work ties it consistently to the onset, depth, and duration of depression, alongside anxiety and lasting stress, because the mind is not resolving the distress so much as rehearsing it. Where processing tells the nervous system that this is over, rumination keeps insisting that it is happening again.

This is where this old Zen story starts to sound like modern neuroscience. Tanzan carried the woman because it was the kind thing to do, and then he set her down. Ekido never touched her, and hours later he was still carrying her, in his mind. His body may well have been responding as though the moment were still unfolding, each replay deepening the same groove, because a memory tends to loosen its grip only when it is revisited in a genuinely different light, not simply repeated.

The irony, and I recognize myself in it, is that so many of us are the second monk. We mistake thinking about something for working through it. We are certain we are processing, when we are only carrying the woman farther down the road. And the mind is quietly invested in the disguise, because picking the weight back up can feel like diligence, like taking the thing seriously, when it is often just fear dressed as care.

I want to be careful not to turn this into an argument for forgetting. There are things that should be carried: grief that deserves its time, lessons that arrive only slowly. The question is never whether to feel something, but how long to keep holding it after it has done its work. We carry what we have to for exactly as long as the crossing requires. And then, on the far side, we set it down. And, harder than any of it, we choose not to pick it up again at every mile that follows.

I wonder, again, what am I still carrying that I should have put down at the riverbed?

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“It is Day 41 of the protests in Albania,” Sue, our guide, explained, as we walked through the wide street. “At 7 pm tonight,” she continued, “thousands of people will gather here to march.” The movement, widely known as the Flamingo Revolution, had already drawn global solidarity; and, since then, Dua Lipa, as a supporter. It began over a luxury resort project planned on protected coastal wetlands sheltering flamingos, monk seals, and nesting sea turtles. However, what started as a local dispute over fencing and bulldozers has become the largest protest movement in Albania since the fall of communism, spreading from a single village to the capital within a matter of days.

But the person who made the protests feel most consequential to me was not a politician, an activist, or one of the influencers documenting them online. It was our guide. Days later, hiking through the dramatic Albanian Alps, she was still the part of that week I have not been able to put down.

Her life seemed to contain many of the pressures behind the movement, and much of the frustration felt by her peers. In passing, she had mentioned her part-time bookkeeping for a Dutch company, accounting work for a Spanish one, and her seasonal work guiding treks. When I asked what her main job was, she explained further and reframed her income stream as a “multi-commitment profile,” with several part-time roles held at once.

But what she was describing was something deeper than a portfolio career. Her generation was distributing risks across employers, countries and possible futures: earning abroad while remaining at home. Her ability to lean into globalization was impressive – whether it was companies hiring her for her skills from abroad, or the tourists, like us, crossing borders to learn about cultures and explore different lands. Her ability to tap into the global opportunities from a local neighborhood in Albania was the actualization of the promise of globalization.

What I saw in her three jobs was the defining adaptation of a generation raised in small, talent-exporting economies. But the more I sat with it, the more it read as an economics story. Over the last few decades, many countries were carved out of larger states, and too many have stayed small in the ways that matter: thin economic opportunity, institutions that never fully reformed, and systems that still run for the benefit of the connected few. When an economy stagnates like that, its most capable young people do the rational thing and leave. In her words, the lack of opportunity and economic stagnation have driven the intelligent and able out of the country, just like the communist regime did. But she refuses to leave. Instead, she brings the global economy to her neighbourhood. Is this the new model, I wondered? Is this globalization 2.0 at work?

The Prior Economic Playbook: The Transfer of Human Capital

Over the years, I have watched an earlier version of this playbook run across many emerging markets. Small, resource-constrained countries that emerged from communism or from the collapse of a larger state tend to follow a standard path: liberalize, privatize, pursue (European) integration into regional economies, and leverage their natural and cultural beauty to fund the transition. Albania has followed this trajectory closely, with tourism becoming a major growth sector and EU accession treated as a core national strategy.

Albania remains one of the poorer countries in the EU accession pipeline: one in five people live below the poverty line. Unemployment is near 11%; with youth unemployment closer to 26%. Emigration is the deeper undercurrent: 2.25 million Albanian citizens live outside the country. Albanians have filed first-time asylum applications across the EU. It is the sort of emigration that international organizations increasingly identify as a constraint on long-term development.

What is absent from the playbook is what happens when the workers who are expected to fuel growth are also the ones most likely to leave. Albania’s population has fallen by 29% over three and a half decades, from 3.3 million in 1991 to 2.3 million this past January, driven almost entirely by emigration rather than falling births. The median age rose from 34.2 to 45 in just twelve years – one of the fastest aging shifts recorded in Europe.

But this is not only Albania’s story. Moldova has lost roughly 35% of its population since independence in 1991. Lithuania has lost about 22% over the same period. Bosnia and Herzegovina’s fertility rate is currently 1.26 children per woman, with its population projected to fall by roughly another fifth by 2050. All four are small countries that emerged from the collapse of a larger political entity. All have struggled to retain working-age people in the face of larger, wealthier labor markets nearby.

This all rhymes with an older story: the collapse of the Ottoman Empire after the First World War produced a new map of modern states, including Iraq, Syria, Lebanon, and Jordan. Nearly a century apart, both waves of fragmentation led to many more flags, and many more seats at the UN table, but less economic opportunity for the same people living there. Independence often creates new national economies without necessarily giving them the scale, institutions, capital, or reforms needed to compete for talent on equal terms with their larger neighbor. Many inherited systems stayed captured or slow to change, so opportunity was pooled at the top while everyone else weighed leaving.

When working-age people leave, countries lose employees, entrepreneurs, consumers and taxpayers at once. Businesses face smaller talent pools, domestic demand weakens and public services must support an aging population with a narrowing tax base. The economy may still grow, but the foundations begin to thin and crack.

The systemic result has been a transfer of human capital from smaller economies to larger ones in an incredibly unfair way: the country of origin bears much of the cost of raising and educating a worker; the destination country receives the productivity, taxes and consumption that follow. Remittances return some value home, but cannot fully replace the economic activity and institutional knowledge that leave with the person.

Statistics like these tend to stay abstract until one of them is standing in front of you, describing her week.

However, the economic promise is that investment, tourism and integration will eventually create enough opportunity for people like our guide to build secure lives at home. But she doesn’t seem to be waiting for that. Her three jobs suggest a more uneven reality: growth may be arriving without creating enough stable, locally rooted careers. Instead, the skill-building enables the youth to stay home will accessing opportunities globally.

Bringing the World Home: The Digital Transfer of Economic Opportunity

My guide will be back at her second job on Monday, reconciling invoices for a company across two time zones, before guiding another group up another trail next weekend. She talked about her schedule like someone who had built a life around not depending on any single employer, government, or country to take care of her. For this new generation, that may be the most rational response available.

There is resilience in that model. It allows people to remain home while accessing opportunities elsewhere. But it also exposes the limits of the domestic economy: a multi-commitment life offers security precisely because no single local commitment feels secure enough on its own. This future of work could help small countries retain talent, but only on one condition: that the income, skills and networks earned abroad are reinvested at home, in businesses started locally, clients served from home soil, and taxes and spending that stay in the country. Where that happens, multi-commitment work is a bridge. Where it only funds an eventual exit, it is a departure lounge with a longer runway.

The question, then, is not simply whether young people stay or leave. It is whether countries like Albania can turn this new way of working into a bridge between global opportunity and local growth, or whether it becomes a holding pattern until a more permanent way out appears.

I keep returning to this as someone who has spent years watching the same current move through my own region. Across the Middle East, the same mobile, multi-committed generation is deciding, contract by contract, whether the world pulls them away from home or lets them build a life rooted in it. So I find myself asking: what would make that global access pull people home rather than away? Can a small country become the place its most mobile people choose to return to, rather than the one they are always quietly leaving? And if this generation can build careers that stay rooted at home while reaching the whole world, might the multi-commitment life become a reason to stay rather than a rehearsal to leave? Is this the point in time where instead of the young and educated vote with their feet to stay home while being able to monetize their skills on the global stage? Is this, indeed, globalization 2.0, at work?

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A large part of the world we live in was funded by venture capital. It is the mechanism that decides which technologies scale and which problems get solved, and its fingerprints are on almost everything we use today. Specifically, 43% of US public companies founded since 1979 are venture-backed, and they account for 57% of total market value. The operating system of the modern world was written in its founding moments, backed by capital that moved before consensus did. The world of tomorrow is being created and funded the same way right now. A company needs seven to ten years to define its market, so what we choose to fund now is what shapes 2035 and beyond. This moment matters much more than it feels like it does.

In 2021, approximately three-quarters of venture dollars went into the ordinary work of building the real economy: finance, health, education, food, and logistics. By the first half of 2026, that share had fallen below a fifth. In the US, Artificial Intelligence (AI) alone took 86% of every venture dollar this year. Add defense, and the imbalance is stark. Globally, we are now directing 77% of our innovation capital toward a single, powerful story about the top of the stack and only 18% towards the foundations and the next version of the real economy.

This is not an argument against AI. AI is real, it is foundational in its own right, and some of it will compound into the most valuable companies of our lifetime. Its share of global venture funding has more than doubled since 2022, and a rational investor can defend every one of these allocations on expected return alone. But capital allocation this extreme is worth examining, because consensus and opportunity are rarely found in the same place. When three deals absorbed 67% of all AI capital in a single quarter this year, that was a measure of conviction, not of opportunity, nor potential value. AI itself reaches into every one of these sectors; what is missing is capital flowing to the companies dedicated to solving the hardest problems these industries pose.

The clearest way to see the gap is to measure it. For every dollar that flowed into AI in the US this year, only eighteen cents went into all other sectors combined. That is the ratio on which the next generation’s economy is being built.

Against the roughly $211 billion that AI companies raised globally in 2025, the rest of the real economy is far behind. Education technology drew just $2.6 billion in 2025, down from $21 billion in 2021, a fall of nearly 88%, at the precise moment a billion workers need reskilling this decade. Supply chain and logistics pulled in only $6 billion in 2025, down from about $28 billion in 2021, after a pandemic that showed all of us what happens when those systems break. Global biotech venture funding has clawed back to $38 billion, close to its 2021 peak, yet biotech’s share of US venture dollars still hit the lowest on record, swamped by AI. The valuations moved in step, with the lack of capital putting downwards pressure on multiples: listed fintech companies that traded eight times revenue in 2021 now trade closer to four. And the founders living inside this compression feel it most. Only a few years ago, a third of seed-stage companies raised a Series A within two years. For the 2022 cohort, this had halved to 15%.

There is an irony worth considering here. In the spring of 2020, Marc Andreessen’s industry-best-known manifesto was a call to build. His central observation was a simple one: the things we make in volume, like computers and televisions, keep getting cheaper, while the things we underbuild, like housing, schools, and hospitals, keep increasing in price. It named the neglected foundations, health, education, housing, manufacturing, transport, and asked everyone the same question. What are you building? Five years on, it is fair to ask what happened to that. Capital heard the call and built AI, and the very sectors the essay told us to fix have taken a smaller slice of venture funding almost every year since.

Now measure that against the opportunity. The future-first sectors are not niche. Financial inclusion means the 1.3 billion adults who still live outside the formal financial system. Healthcare access means the 4.5 billion people who are not fully covered by essential health services. Digital infrastructure means the 2.6 billion who are not yet online, and therefore not yet inside the AI economy everyone is racing to build. Food and agriculture mean producing 60% more food by 2050, grown on less land and with less water. The future of work and education means that one billion people will need reskilling, as 22% of all jobs are transformed by 2030. These are not lines in an impact report. They are among the largest and most durable markets on earth, trading at early-stage multiples because attention has moved elsewhere.

None of this asks anyone to abandon the trend. There is real value in backing an AI wave and exploring the new native technologies it creates. That is part of the job, and some of it will define the next decade. The risk is quieter than a bad investment. It is letting a genuinely exciting distraction crowd out the economy and future that everything and everyone depends on, until we look up and realize we financed the fourteenth model while allowing the foundations to age and crumble, underfunded, beneath us.

So what is the discipline here? Can we chase the AI frontier and still keep the real economy in view, and weigh it in every investment decision we make? Can we ask, of every investment, whether it builds something the next generation actually needs? The better future is already being assembled, quietly, in a thousand companies solving problems too important to be fashionable. And what is investing future-first, if not choosing to forget them?

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Some of my earliest memories are of Damascus. Of the heat, the noise of the souq, and the scents of the crowd. Returning each summer as a child, born and raised outside the region, with broken Arabic in a city where time stood still and I stood out, I would walk quickly and listen carefully as my grandmother would share her wisdom, her knowledge, and tales of our lands as we moved through the sea of people. The story she told most often was of the hakawati.

Picture him, she would say. Right there, she would point.

Picture a man climbing into a tall chair in this coffee shop, which has been here for centuries. Imagine him opening a worn book and lowering his voice until the whole room leans in to catch it. He carries a hero into battle, but then stops at the edge of the danger and closes the book, saving the ending for the following night so that every seat fills again. I never saw him myself, of course. Yet even then, the thought of him mesmerized me.

The word hakawati comes from the Arabic hekaya, a story, and haki, to speak. For centuries, across the Arab world, the hakawati was the person a community gathered around in coffee houses, palaces, and town squares to hear news, stories, and history. With little more than an expressive voice, gestures wide enough to fill the room, and sometimes a drum or a stringed instrument keeping time beneath the words.

What he told was rarely a single story. It was a web of them: myth folded into history, one tale opening inside another, a hero’s saga suspended each night on a cliffhanger so the room returned the next evening. This was how a community kept its memory. The thousand and one nights of Scheherazade, who told nested tales to stay alive, the sense of who the heroes were and why, all of it carried forward not on paper but in performance, night after night, generation after generation. Long before radio, television, or a feed that refreshes forever, this was how people remembered.

The Holder of Memory

Calling the hakawati a storyteller undersells the job. He was the newspaper, carrying word of battles and rulers from one town to the next. He was the history teacher, keeping centuries of lineage and legend alive at a time when almost no one could read. He was the moral instructor, using the courage of a hero or the wit of Scheherazade to show a room full of strangers what a good life looked like, and what a coward’s end deserved. And he was the thread that turned those strangers into a community, handing them a shared cast of heroes, a common set of references, a single story they all belonged to.

That is the real value of storytelling, and it has little to do with charm. Strip the performance away, and the function remains: a story is how a community transmits what matters most, its knowledge, its values, and its sense of itself, in a form that stays with people. Every culture that has ever existed wrapped its faith, its laws, and its warnings in narrative. The hakawati was the trusted carrier of that narrative.

The Soft Skill

Then, somewhere in the move to boardrooms and business schools, storytelling lost its standing. It became a “soft skill,” which in a professional context is faint praise dressed as a compliment. The trust started to erode.

We started associating it with “selling sand back to the desert.” With spin. With the smooth operator who can make any number sound like a good. “Narrative” began arriving with a raised eyebrow. To say a founder is “telling a nice story” about their revenue is not a kind observation. We moved our respect toward the hard and the measurable, and quietly filed storytelling under manipulation – a talent to be watched rather than admired.

For most of human history, the storyteller was a person who lived in your community, who needed to earn and keep the trust of the community. Who felt accountable to the community. As the storyteller could start to hide behind walls or disappear, the accountability to the integrity of the story started to erode.

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The Story is the Product. Again.

And then the ground moved back.

’s Prof G Media recently made the case that would have sounded absurd a decade ago: storytelling is the most valuable skill in business. The argument is that distribution has become the moat. When AI makes it cheap and fast to build almost any product, the product itself becomes a weaker way to stand apart. What separates companies now is the size of the audience and the loyalty of the following.

The numbers are worth considering deeply. Founders now post six or more times a day and spend roughly 70% of their workday on content, and 63% of people say they trust an influencer’s opinion over a company’s advertisement. A 2017 study from Wharton, Harvard, and Indiana found that a one-standard-deviation increase in a startup’s attention on Twitter was associated with about $1.5 million in additional funding. By the founders’ own accounts, the returns compound from there: Cluely, whose slogan is “cheat on everything,” reached 70,000 sign-ups in a week and raised more than $20 million; and a pickle brand built a following of 100,000 and achieved best-seller status with zero ad spend.

The venture establishment has stopped treating this as a stunt. Andreessen Horowitz now runs a dedicated team to handle founders’ media operations, offering distribution the way it once offered only capital. Two-thirds of Fortune 100 CEOs had a social profile in 2025, and the creator economy passed a quarter of a trillion dollars in 2026.

So the hakawati’s craft, demoted for a century to a soft skill – and something to watch out for – has quietly become the hardest currency in business. I see this in my own work as an investor. The company that cuts through is often not the one with the best product, but the one with the best story, and the gap between the two is widening, not closing. Scott is right. The story is the product.

Human Nature

None of this should surprise us. Why? Because we, as humans, are wired for it.

The cognitive psychologist Jerome Bruner argued decades ago that we hold on to information far more tightly when it arrives in a story than when it arrives as a bare fact. Anyone who has taught a child knows this without the citation. My own children forget an instruction within the hour and remember a lesson wrapped in a story for years. For thousands of years, we passed knowledge around fires and in cafés precisely because narrative is how a fact survives the night. We are storytelling creatures first and reasoning ones second, and we have been for as long as there have been fires to sit around.

Lost Accountability

This is where the old café and the modern feed part ways. The hakawati and the influencer are doing the same job: carrying the stories a community lives by. Where they diverge is accountability. Whereas technology has enabled reach to scale to numbers the hakawati could never have imagined, it has created incredible distance, and accountability has fallen close to zero.

The disparity is obvious and sad. In global surveys this year, 47% of people named online influencers and personalities as a top source of false or misleading information, level with national politicians. Around 67% say they are worried about online disinformation, and across 35 countries, a median of 84% of adults say made-up news is a serious problem in their country. AI has poured accelerant on all of it. NewsGuard tracked more than 3,000 AI content-farm sites by March 2026, up from about 2,000 five months earlier. The OECD now logs close to 500 media-reported AI content incidents a month, roughly ten times the rate of early 2020. About 70% of people admit they have already been misled by something they read online.

The hakawati could be found. If he twisted a story, the neighborhood knew where he lived, and he would never be welcomed back if he was a traveler. Today’s storytellers cannot be found so easily. The influencer with a million followers, or the AI model producing a thousand posts an hour, answer to no one. We still learn, remember, and decide through stories, exactly as we always have. What we have lost is the accountability that once came with telling them. The hakawati and the influencer do the same job, but only one of them has been held responsible. If reach has replaced accountability, who answers for the integrity of the story now?

Integrity

Back in Damascus, the hakawati in my mind closes his book on a cliffhanger and steps down from the chair. The tradition has held that room for two centuries, one teller after another, each earning the room in a place where everyone knows his name and can knock on his door.

Maybe storytelling was never the smaller, softer skill we filed it under. Maybe the real skill was always the harder one, and it was never about the telling. It was about being willing to be found and held accountable for the integrity of what you share.

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“Let me tell you the secret to going deeper into the pose,” said Bryan Kest, a power yoga pioneer whose teaching and methods I have been following since they were first released on VHS tapes. Yoga has been a part of my life for twenty-six years now, yet, as Bryan led us through this sequence, I listened with full attention. I wanted to go deeper. What was the secret?

I leaned into the pose intentionally.

“I can tell the secret is something you are all seeking. And I will share it with you,” he teased. “Because you are all here for the same reason. To balance. Your body. Your life. Your mind. But there is something many of you are missing.” He carried on, testing our patience.

He was right. Everybody here was on their mat hoping to become a slightly better version of themselves. And I was no exception. I have spent a career optimizing and refining; and the promise of doing something better is one I have never learned to walk past. What was the secret to going deeper? How had I not picked this up in the decades I had been following his practice? I listened eagerly.

He continued, “The secret to going deeper into the pose…is…to find…the space. The space between the muscles. The space within your body. The space that already exists. You see, when you find that space, you will find that space is light. And in that space and light, you will find room to grow and see. To stretch. In that space, you will find the opportunity and freedom to explore. To go beyond.

“The opposite of space,” he carried on, “is contraction. And contraction is density. Heaviness. When something is dense, it does not allow light through. Density is dark. There is no space for light, for expansion, for growth.”

I have let a hundred yoga cues float past me, but this one stuck. Because the moment he said it, I was no longer thinking about my hip opening or my back leg extending. I started struggling to maintain my regular breathing. My anchoring inhales and exhales started escaping me. My mind started drifting. I was now thinking about my calendar. The density there. And the density everywhere else in my life that I had, until now, been unaware of.

If space is where the light gets in, my thoughts travelled, then density is the inverse: more darkness, more anxiety, little room to see. When a pose is contracted, the muscle has nowhere to lengthen. When a day, a conversation, or a decision is contracted, the mind has nowhere to lengthen or expand either.

My week is dense by design, and I suspect many people reading this build theirs the same way. Back-to-back is a badge; a full calendar feels like proof of relevance, of being needed. We compress the day until there is no air in it, then wonder why we feel we are reacting - rather than seeing or observing.

The question Bryan had posed was not ‘how do I relax or go deeper,’ but something harder: if I have engineered the space out of my days, how do I expect to see clearly? Where is there space or light that seeing even happen?

What he was describing runs deeper than the calendar. It is the willingness to hold a space open a little longer before filling it: in a day, in a conversation, in a decision, in a relationship.

Useful Physical Space

The idea that space or emptiness is the useful part of something is not new. The Japanese have a word for it: Ma (間), the negative space, the gap, the interval. In their design and aesthetics, the empty space between the strokes of a character matters as much as the strokes themselves. The pause is what gives the marks their harmony and balance. Remove it, and the whole thing collapses into noise.

The body works the same way. Muscle does not grow while you train it. It grows during recovery while at rest. The effort only sets up the adaptation. The empty stretch afterward is where the growth actually happens.

But yet, almost everything we are rewarded for is the visible: the weights lifted, the strokes on the page, the meetings, the deals, the output you can point to. As a result, the space is the first thing I give away when the week gets heavy, because it looks like time spent doing nothing. Not productive. Yet that emptiness, I now realize, is doing the real work. The work the strokes cannot do alone.

Creating Space

So where does the space come from when the calendar cannot accommodate it? Where can we actually create space? The most consistent answer I have found across the years is the breath: the very thing Bryan keeps asking us to come back to on the mat. Yoga built an entire discipline around it: pranayama. Breathwork. Bringing consciousness and awareness to the breath can create space in the mind and body.

In a recent conversation, Ricardo Sunderland, author of The Energy Advantage, pointed to the same thing: intentional breathing is one of the simplest routes back into the body, and into energy we otherwise walk past. A long exhale opens room; the still point at the top of an inhale is, quite literally, space. It asks nothing of my schedule. Just my attention.

The same logic governs how I lead. In any real conversation, the space is where the other person becomes audible. If I answer my own question before the founder or the team member across the table has finished forming theirs, I have run the meeting and learned nothing. Creating space in the conversation is how I discover what the other person is thinking or feeling. The most useful thing I can offer them is often a pause long enough for them to hear their own thinking out loud. The most interesting reflections and ideas are often shared after the silence.

Density, on the other hand, does to a team what it does to a body: it removes the room for people to grow. Take the space away, fill it with status updates, and you get motion without development. Busy, contracted, and quietly going nowhere.

A leader who contracts every conversation gets compliance and calls it alignment. A leader who creates space gets the truth, which is slower, less flattering, yet far more valuable.

Holding Space

Then, of course, there is the idea of “holding space,” as if it were a wellness phrase. This is closer to a discipline, and a difficult one: resisting the urge to fill the room with my own voice so that someone else can reflect, share, and arrive somewhere on their own. Actually, it is closer to resisting the urge to fill my mind with my own thoughts and reflections on what is being said. Holding space is truly about giving a non-judgmental, non-thinking space for somebody else to be their authentic self.

Giving someone your full attention without any judgement allows them to feel seen; and you will likely learn something in return. It asks for nothing but your presence.

Space To Decide

The space I find hardest to protect sits inside my own head: the speed at which I move from information to conclusion, from problem to answer, leaving no gap. That gap is also space, and I close it faster than anything else in my day, because closing it feels effective and competent.

I see this most clearly in investing. An over-scheduled group is fast at processing and slow at perceiving; it can clear an agenda and still miss the one thing that mattered. The opportunities we are proudest of rarely announce themselves in the room where we were meant to find them. They surface in the gaps. The space.

So I protect that gap rather than skip it. When a conclusion is available too early, I try to leave the decision unmade for a while, to sit in not yet knowing, rather than grab the first answer that fits. Some questions are simply better answered tomorrow. After some space.

What unsettles me most about density is that it does not announce itself. A tired body tells you it is tired, but a contracted life tells you it is full, which feels like the opposite of a warning: it sounds complete, accomplished. You just make a slightly worse decision, have a slightly shallower conversation, miss a slightly larger signal, and move on, in the dark, without ever realizing that the light was off. How often are we in the dark without even knowing it, not in crisis, not obviously depleted, just operating with the room dimmed, mistaking a full calendar or a fast answer for a clear one?

Creating space in a life this full is not a grand gesture. The real work is smaller and more stubborn. It is the ten minutes between meetings that are actually empty. The walk taken for its own sake (not for step-counting). The question slept on overnight, and answered tomorrow. The silence I do not rush to fill. None of this looks productive. But all of this is where the light shines. Where the seeing happens. And where the growth lies.

Back on the mat, I stop fighting. I stop forcing my way deeper through strained effort, which only produces more contraction. Instead, I search gently for the space, the small amount of room that is always there if I am willing to find it. And in that lightness, almost without trying, but with a deep exhale, I find it and I go deeper.

The pose was never the point. Finding the space was.

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Health is one of the sectors I invest in. Last weekend, I was speaking at a summit in the beautiful countryside of Wales. The panel was about women’s health. The organizers called it “the health of the 51%,” which is a generous way of describing the majority. The moderator turned to me and asked how I would reimagine the healthcare system for women.

I gave a reasonable answer. It has stayed with me all week, because the honest answer is duller and harder than the one I offered: it is an accounting problem. For every dollar the world spends on private healthcare, only 6% goes to conditions specific to women. And of the research and innovation behind tomorrow’s treatments, only about 1% addresses women’s conditions once you set cancer aside. Yes, just 1% of health research funding reaches women’s health. Half the population. 1% of the attention and budget.

That gap is usually told as an injustice, and it is one. However, it is also one of the largest mispriced opportunities I have come across, and I am lucky to be looking at it early. To see why, follow a single problem from its root.

The Starting Point: The Young Male Reference

For most of modern medicine’s history, lab animals were male, cell lines were male, and the human volunteers in clinical trials were male, and even now, studies using only male animals outnumber female-only ones by about five to one, with barely 15% including both. The instinct to research this way was practical rather than hostile: female bodies, with their cycles, were treated as a variable to control for – and a variable scientists could eliminate.

But eliminating the variable from trials did not mean eliminating the impact of lack of research. Some of the most common examples include that the US withdrew between 1997 and 2000 proved more dangerous for women; that the FDA did not halve women’s dose of Ambien until 2013, two decades after approval; and that women still experience adverse drug reactions nearly twice as often as men, largely because doses were set on male bodies. Even the crash-test dummy that signs off on a new car was modeled on a man. None of this was malice. It was a default, set once and rarely revisited, and a default repeated for a century quietly becomes the way things are and have always been.

Early Adulthood: Adolescence

The neglect starts early, in adolescence. A girl’s first cycle arrive with almost no framework for understanding it, and the questions that come with it, about what is normal, what is not, and when to worry, are largely left for her to work out alone. Period pain gets waved off as something to be endured, which is part of why a condition as common as endometriosis, affecting roughly one in ten women, still takes seven to ten years to diagnose.

I saw the scale of that gap up close. For three years I was the independent chair of Clue, the menstrual-health app that more than 15 million women use to understand their own cycles. What struck me most was the demand: an enormous appetite for clear, trustworthy information that medicine had simply never bothered to provide, and how little anyone had built to meet it. Without options like Clue, women start taking a pill to manage these symptoms – a pill that has side effects and repercussions for many years down the road of life. A pill that is frequently handed out young, with little discussion of the trade-offs. A pill that tells the body to stop the cycle as it should be.

Mid-Adulthood: Life and Medication

This pattern compounds across a life. On average, a woman is diagnosed about four years later than a man for the same condition, and a 2024 study of more than 21,000 emergency visits found women were less likely to receive pain medication than men reporting the same level of pain, and less likely to have their pain recorded at all. The stakes are not only comfort: heart disease is the leading cause of death for women, yet a woman is 50% more likely to be misdiagnosed during a heart attack because the textbook symptoms are a man’s. None of this needs a villain. It is what happens when half of all patients are treated as a variation on the other half.

Late Adulthood: (Peri) Menopause

At the far end of that arc sits the transition we study least: perimenopause and menopause. This is not just about hot flashes and broken sleep. The fuller picture is more than two dozen documented symptoms running head to toe, from broken sleep to thinning bone to rising cardiovascular risk. More than a billion women are in it or beyond it today, and nearly 500 million will be aged 45 to 55 by 2030. Yet fewer than a third of US gynecology programs teach it, only about one in four women is treated, and the cost surfaces elsewhere, around $26.6 billion a year in US medical bills and lost work. A universal event – left largely unstudied. Affecting 51% of the population at some point in time.

Yet, the best we can do so far is hormone replacement therapy (HRT), the prescription estrogen and progesterone many women take to manage symptoms, which is set to grow on its own from about $25 billion in 2025 to roughly $35.6 billion by 2030, and that is only the prescription slice. The example I keep returning to is The Better Menopause, which makes science-led supplements for the peri- and menopausal years, sold direct, in a category long underserved by products built on real evidence. It is running the first double-blind, placebo-controlled clinical trial for a menopause supplement, with the University of Southampton; in a low-trust category, that proof is the entire proposition. It is led by Debbie Wosskow CBE, who has built and exited before and is doing the slow, credible work this space has been waiting for.

Late Life: Wisdom

We tend to write off the years that follow as decline. The evidence says otherwise. For most of human history, the women past their reproductive years were the ones holding families and communities together. The leading evolutionary explanation for why women live decades past fertility is the grandmother hypothesis: older women raised their grandchildren’s odds of survival so reliably they may have helped lengthen the human lifespan itself. Read that way, menopause looks less like a “malfunction” and more like a design feature.

How many of us turned to our grandmothers for wisdom and advice when we were young children? They not only held the knowledge of which tea and herbs would calm each of our ailments, but also the wisdom to hold space for each of us in our communities.

Some places still build on it. In Zimbabwe, a psychiatrist named Dixon Chibanda ran short of doctors and turned to grandmothers instead, training them to deliver talk therapy on simple park benches outside clinics. In a randomized trial, 14% of the people they treated were still depressed after six months, against 50% in usual care, and the model has since spread to New York, Washington, and beyond. Treated as an asset rather than a cost, older women turn out to be a kind of health infrastructure the formal system struggles to build.

Why This, Why Now?

So why has the neglect lasted, and why is it cracking now? Part of the answer is that money follows power and markets.

Think about the way you monitor your health – all your devices: we have made full and continuous glucose monitors (CGM) mainstream; anyone can now watch their blood sugar move on their phone in real time. In addition, you can monitor your sleep, your recovery, and all the other metrics you choose. Yet, hormones, which govern mood, sleep, fertility, and the whole into-cycle and out-of-cycle (menopausal) transitions, are still captured by the occasional blood draw, if at all. Ask how much science and money went into making glucose continuously visible, then ask how much went into the hormones that shape half of human health. The gap between those two answers is the opportunity. It is starting to close, with startups like Clair Health raising $11.6 million to build continuous, non-invasive hormone monitoring, inside a femtech market projected to grow from $39 billion to $97 billion by 2030. Continuous hormone data could do for women’s health what the CGM did for metabolic health, and almost no one has built it yet. Because nobody has paid attention. This is one of many examples of the massive gaps and opportunities that exist.

Now, that starts to change.

Women now control more than $30 trillion in global spending and are on track to hold 75% of discretionary spending by 2028. Women are also in leading positions in large corporations, backing not just medical research, but advertising, pharmaceuticals and other key influencing industries. Just twenty years ago, fewer than 1% of the largest US companies were led by a woman; today it is at 11%. Worldwide, over a similar span, women’s share of global private wealth has risen from roughly a quarter in 2010 to about a third today.

Now, this shift has been dramatic not only in the numbers, but also in the voices necessary to make this change.

As that influence concentrates, a market long treated as marginal becomes impossible to ignore. The capital has started to move: Melinda Gates has committed $215 million to women’s midlife and reproductive health, part of more than $600 million in two years, and used her platform to call for a menopause revolution. The question is no longer whether this becomes a category. It is who builds the winners, and how soon.

That is where it gets interesting to me as an investor.

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It was April 2008, and we had just taken Depa public, raising almost $500 million in primary capital for the company. It was the first business from the Middle East to list simultaneously on the London Stock Exchange and NASDAQ Dubai, and it was the world’s largest interior contractor: 9,500 people, 22 markets, lead bankers, the works. For our team, it felt like the culmination of years of work, hours, and sleepless nights.

The reception was extraordinary. Investors wanted exposure to the growth story. Depa had the capital it needed to keep consolidating a fragmented industry across the world, continue to expand internationally and into new verticals, and strengthen its position as one of the world’s leading interior contracting businesses.

Then, the aftermath happened. Within months, the world was in the middle of the worst financial crisis since the Great Depression. Lehman Brothers collapsed. Credit markets froze. Trillions of dollars evaporated.

Depa had been one of the last major IPOs to reach the market before the crash, alongside Visa, which weeks earlier had completed the largest IPO in U.S. history. Five months after our listing, Lehman Brothers filed for bankruptcy. At the time, few saw it coming. We had made it in time. But you can’t time the market. That is the thing about markets. The peaks and turning points are only obvious in hindsight.

Which is why I find myself thinking about 2008 now, as SpaceX arrives on the public markets in what may be one of the most significant offerings of this generation. On 12 June it listed on the Nasdaq at roughly $1.77 trillion, the largest initial public offering in financial history (reminding me of Visa’s back then), and closed its first day up nearly 20%.

The parallels are not exact. But they are interesting.

The Liquidity Event the Industry Has Been Waiting For

As a venture capitalist, I could not be more excited about the SpaceX IPO.

Why? Because for years, venture capital has been suffering from a drought of liquidity. Fundraising has slowed dramatically outside of AI. Distributions have remained stubbornly low. Many limited partners have found themselves overallocated to private markets, simply because public markets recovered from the last corrections faster than private valuations adjusted, and companies are staying private longer.

The result has been a system starved of DPI — distributions to paid-in capital.

A listing like SpaceX’s changes that almost overnight.

The timing matters. In 2025, only approximately 30 venture-backed companies completed U.S. IPOs, less than half the long-term historical average, and distributions to limited partners fell to 6% of assets, against a long-run norm closer to 14%. For firms such as Sequoia, Founders Fund, and DFJ Growth, a SpaceX listing is one of the largest realized liquidity events in venture capital history. Founders Fund’s early bet alone became a stake worth more than fifty billion dollars, Valor’s more than ninety.

Billions could flow back to pension funds, endowments, sovereign wealth funds, and family offices. LPs would regain liquidity. Confidence could return. Fundraising for venture capital funds and investments could accelerate.

In many ways, SpaceX may be the liquidity event the venture industry has been waiting for.

What Happens Next

And yet I cannot help wondering whether the more interesting question is not the IPO itself, but what happens next. Because liquidity has consequences. More liquidity creates more optimism. More optimism creates more risk-taking. More risk-taking creates higher asset prices. Higher asset prices improve financial conditions and encourage even more capital formation.

In other words, a successful SpaceX IPO does not merely reward existing investors. It potentially injects fresh energy into an already buoyant market.

History suggests that major liquidity events often reinforce the prevailing narrative of a cycle. They validate what investors already want to believe.

The late 1990s had its flagship technology listings. The mid-2000s had its landmark financial transactions. Our cycle may have SpaceX.

And if SpaceX becomes the event that unlocks liquidity across venture capital further hyped by a focus on retail investors, it may also become the event that adds fuel to an already hot market. In turn, it might suddenly and unintentionally unlock risks in adjacent markets that seem uncorrelated at this time.

Where the Risk Might be Hidden

Over the last decade, private credit has grown into a multi-trillion-dollar asset class led by firms such as Blackstone, Apollo, and Ares. At the same time, companies have stayed private far longer than previous generations. The private markets have effectively become the new public markets, growing to more than $14 trillion in assets. Companies such as SpaceX, Stripe, and Databricks reached valuations that would once have demanded a public listing, yet stayed private while continuing to raise billions.

For years, low interest rates made leverage relatively painless. Debt was abundant. Capital was cheap. Valuations expanded. Private equity firms could finance acquisitions confident that refinancing would always be available.

But leverage is rarely dangerous when times are good. It usually becomes dangerous when conditions change.

If rates stay higher for longer, or rise further, fueled by a buoyant and excited market, the refinancing math becomes significantly more difficult. Debt becomes more expensive. Cash flows matter more. Valuations come under pressure. Companies that looked healthy at one interest rate can look very different at another. And this is the world we are in: the Federal Reserve has held its benchmark rate at 3.5% to 3.75%, with inflation still above its target and the cuts the market keeps expecting refusing to arrive.

Private-equity-backed companies face hundreds of billions of dollars of debt maturities in the coming years. Much of that debt ultimately sits within private-credit structures.

And here is what would make this cycle’s blind spot different from the institutional crises of the past: who now holds the risk.

For most of its history, private credit belonged to institutions, the pension funds and insurers and sovereign funds built to lock money away for years and absorb the occasional loss. Lately it has been migrating into other hands. The lending has been repackaged into semi-liquid funds built for ordinary investors, often open to anyone with a few thousand dollars to put in; by the end of 2025 those funds held more than $534 billion in assets. And in August 2025, an executive order with the cheerful title Democratizing Access to Alternative Assets for 401(k) Investors began opening the door for this kind of lending to sit inside ordinary American retirement accounts.

The IMF has flagged the same vulnerability, noting that these loans are often valued by estimate rather than by a real market price, so strain can build for a while before it surfaces on anyone’s statement.

The last time a hard-to-value form of debt was quietly moved out of expert hands and into everyone’s, with assurances that it was safe, the assurances did not hold.

An Early Warning

And we may already have seen an early warning.

In November and December 2022, Blackstone’s BREIT vehicle hit its redemption limits after investor withdrawal requests exceeded preset thresholds. The assets themselves did not disappear. The issue was liquidity: investors wanted cash faster than the underlying assets could be sold.

The pattern returned more recently. Late last year and into this one, Blackstone lifted the withdrawal cap on its flagship credit fund from the usual 5% a quarter to 7.9% to meet a surge of requests, while Apollo, Blue Owl and KKR moved to cap withdrawals in theirs. For the first time, the sector paid out more than it took in over a single quarter.

That does not mean a crisis is coming. But it does remind us that liquidity and solvency are not the same thing.

The housing market was not the problem in 2008. It was simply where the leverage happened to be hiding, in investment pockets that weren’t designed for them

Today, few people worry about subprime mortgages. But every cycle has its own blind spot. Every cycle has an asset class that begins as a genuine innovation. Every cycle has a corner of the market that grows because it solves a real problem. And every cycle has a form of leverage that feels entirely manageable — until suddenly it isn’t.

Private credit has undoubtedly solved a real problem, particularly as it has ballooned over the last decade But I cannot help wondering whether future historians will look back at this era and ask the same question we now ask about 2008. Was private credit to this cycle what subprime mortgages were to the last? And will this IPO create further hype that drives up interest rates to a degree that it affects everyone?

Not the same asset. Not the same structure. Not the same outcome.

But the same place where leverage quietly accumulated while most investors were looking somewhere else.

Where I Might Be Wrong

And yet I have learned to be suspicious of an argument that fits this neatly, so let me put the other side.

The comparison to 2008 is imperfect, in ways that matter. The gates I just described as a warning are also a genuine safeguard; they stop a fund from dumping its assets in a panic, which is precisely the spiral that made the last crisis so violent. Private credit is far less leveraged than the banking system was. Much of it is funded by money committed for years, which cannot run for the door the way bank deposits did. And the loans mostly carry floating rates, so higher rates lift the lenders’ income even as they squeeze the borrowers.

So the danger, if it comes, may be quieter than a crash: a slow erosion that ordinary investors absorb over years without ever quite noticing. Or I may simply be pattern-matching, seeing 2008 because 2008 is the year I carry with me. Not every top ends in a crisis. Some simply deflate.

Can’t Time the Market

I hope the answer is no. I hope SpaceX marks the beginning of a new era of innovation, liquidity, and growth. But whenever I see a truly historic IPO, I find myself remembering April 2008.

The IPO was finally done. We had made it to market. This was the first time anybody had done anything like this. I couldn’t believe it.

Neither could the market.

And a few months later, the world discovered where the debt had been hiding. You simply can’t time the market.

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“I’m bored.”

My son repeated himself louder, in case I hadn’t heard him the earlier times.

“Great!” I responded with forced enthusiasm, “Because boredom precedes…”

I could almost hear his eyes roll.

“…creativity.” He completed my sentence.

As a mother of three boys, “I’m bored” elicits many responses, but this one is, by far, my favorite. Because it is true. When we are bored, our mind rests, wanders, and creates. It connects dots it hasn’t connected before. Yet, with our access to permanent and continuous distractions, it is difficult for our mind to be bored—to rest, to wander.

My parenting response made me wonder: if this was true for my children, was it not also true for me? Given how much we expect of our brains, and how addicted we are to productivity, have we mistakenly come to see boredom as a waste of time? There is always so much to do, so much to solve. And we have convinced ourselves that boredom means idleness, and so we push.

And we are very good at pushing. We push for one more meeting, one more email, one more flight, one more decision after everyone else has gone to bed. We brag about surviving on four hours of sleep, stack meetings wall to wall, answer email at midnight, and assume that because our bodies are sitting still, we are resting. The mind behind the still body is often running a marathon.

The result is a low-grade mental fatigue that almost never announces itself as fatigue. You do not think “my brain is tired.” You think you cannot focus, that you are unmotivated, that you are procrastinating, that a decision which should be simple somehow is not.

The cost is not just a worse afternoon. The average knowledge worker now switches tasks about every three minutes, and each switch forces the mind to drop one context and rebuild another before it can settle back into anything deep. When meetings run back to back, stress-linked activity in the brain climbs steadily, and when short breaks are slipped between them, it stays flat. At the far end, the body sends the invoice: working 55 or more hours a week carries a 35% higher risk of stroke and a 17% higher risk of dying from ischemic heart disease, and in 2016, long hours were linked to roughly 745,000 deaths worldwide. The evidence tying overwork to depression is more modest and still contested, and I would not lean on it. The cardiovascular bill is not in question.

“The brain is (not) a muscle.”

We hear it constantly. Use it. Train it. Push it. The promise buried in the phrase, “The brain is a muscle.” It is the promise of the gym: stress the thing, exhaust it, let it repair, and it returns stronger. It is a reassuring idea, but for the organ it describes, it is the wrong one.

We have become fluent in how to recover the body. We know what to do for tired legs and a strained back – there is a whole industry to help us. Ask what we do, deliberately, for a tired mind, and most of us go quiet.

A muscle grows through damage. Lifting a weight creates microscopic tears in the fiber, and recovery repairs them a little stronger than before, ready for a heavier load. Strength comes out of that quiet cycle of stress, repair, and adaptation.

The brain does not run that loop. Neurons do not grow stronger for having been damaged. Chronic stress, sleep deprivation, and cognitive overload do the opposite, eroding memory, attention, creativity, and judgment. A mind pushed to exhaustion does not come back sharper. It comes back slower, and simple things take longer.

The brain grows differently: through learning, connection, and consolidation. A muscle repairs tissue; the brain reorganizes networks. And much of that reorganization happens not while we are working, but while we are recovering. During sleep, the channels between brain cells widen and fluid washes metabolic waste out of the tissue, a nightly cleaning cycle that runs only once we stop. The neuroscientist who first mapped it described the brain as having to choose between being awake and aware, or asleep and cleaning up. No app replaces that.

“It Came to Me in the Shower!”

It is why the breakthrough so rarely arrives while you are staring at the spreadsheet. It comes on the walk afterward, in the shower, on the drive home, in the first quiet minutes after waking. We file those moments under interruptions to our productivity. They may be the engine of it.

To be fair, the metaphor of the brain as a muscle is not entirely wrong. The brain does adapt to use, and challenge is how it learns; that is the real truth behind “use it or lose it.” The difference sits in the recovery model. A muscle is forged by being broken down. A mind is forged by being restored. When we confuse the two, exhaustion starts to look like progress.

“Sleep on it.”

So what does recovery actually look like on a Tuesday afternoon, wedged between two calls that both matter? And why is one of the oldest adages “sleep on it” so true?

The encouraging answer is that we already know. We have simply built a world that quietly removes the conditions under which recovery used to happen on its own. For most of the 300,000 years humans have existed, recovery was not a practice anyone scheduled. It was the texture of ordinary life. People walked long distances because walking was how you got anywhere. They spent their days outdoors, sat around fires in the evening, fell into long stretches of silence, and slept under genuinely dark skies. None of it carried a label.

I am not romanticizing that life. It was shorter, harder, and more dangerous in ways no recovery score would forgive. The point is narrower: a handful of conditions that happen to restore the mind were simply ambient then, and we have since engineered most of them out. What is striking is how often modern neuroscience, handed an MRI machine, ends up validating the oldest habits we abandoned.

Sleep is the foundation. During our sleep cycles, the brain sorts, stores, and recalibrates the emotional residue of the day. Neuroscientists Matthew Walker and Els van der Helm call this “overnight therapy”: we wake with the memory intact, but its grip on us has loosened. This is why a difficult conversation looks different after a night’s sleep. The facts have not changed, but our capacity to hold them has. Better decisions follow because sleep cleans the headspace, protecting the conditions under which it can do its vital work. The goal is not a recovery score on your Whoop or a sleep grade on your Apple Watch. Just sleep.

Walking is the clearest case. It lifts creative output by around 60% compared with sitting, indoors or out, and the movement itself does the work, not the scenery. Nature does something quieter: Rachel and Stephen Kaplan’s Attention Restoration Theory draws the line between the effortful attention an inbox demands and the “soft fascination” a shoreline or a garden invites, the second letting a drained attention system refill. This is walking for walking’s sake, untracked and unhurried. I love running too, and find it meditative in its own way, but this is just walking. Bring presence to the walk itself.

Silence may matter more than we assume. Two hours of queit each day prompted new neurons to form in the memory regions of mice, and while that is an animal finding I would hold loosely, it points the same direction as everything else. Even awe earns its keep: looking at something vast, a mountain range or a night sky, quiets the brain network tied to self-focused rumination, which may be why it leaves us feeling smaller and lighter at once.

And then there is fire, which our ancestors gathered around every night. A fire commands attention without demanding it. It is, in a sense, the original screen, except that it slows the nervous system instead of hijacking it or numbing it.

The oldest tool of all is the breath, older than every tradition that later built rituals around it. Slow, deliberate breathing shifts the nervous system out of “fight or flight” toward “rest and digest,” a change you can see in the data as a rise in the heart rate variability that tracks it. I practice. Long before anyone could measure vagal tone, the yogic tradition had found the same lever – Pranayama, the discipline of controlling the breath to direct prana, our vital life force – and learned to pull it. I treat it as recovery before asking anything of my brain, starting my day with it, rather than as one more thing to optimize before the day starts.

Pranayama or meditation? It does not matter, as long as I sit and allow myself the time to observe my mind without judgement, to bring the life force in, to center and ground before the day begins. How long? For as long as I can or choose – some days it is five minutes, some days forty. But that’s not the point. In our obsession with accuracy and measurement, we can sometimes forget that habits are formed in small steps, and that what matters is consistency, that we do something. In our obsession with doing this “right”, we sometimes choose not to act at all.

The same instinct shapes how I work: some of my hardest conversations happen on a walk rather than across a table, because a moving, settled nervous system handles difficulty better than a braced one.

None of this is a gimmick. It is a way of living we used to inherit and that we now have to actively choose.

“Ask me tomorrow”

Which is what makes our current obsession so revealing. We have become extraordinary at recovering the body. We wear a ring to grade our sleep, a band to score our recovery, and we read our heart rate variability over coffee. We book the massage to thank the body for its work, take the ice bath, weigh the protein. We have built an entire industry around restoring the system a knowledge economy leans on least.

There is even a trap hidden inside the devotion: when every walk becomes a step count and every night’s sleep a number to beat, the measuring can drain the very pleasure that made the thing restorative in the first place. We turn rest into one more performance for the body to win at the expense of the brain.

And the organ we lean on most, the one deciding and creating from the moment we wake, gets no equivalent practice at all.

And so decision fatigue sets in. I have experienced it enough to have found some relief in a few practices. The first is knowing that some questions are better answered the next day, and relishing in “ask me tomorrow” when my brain is fried. Why make a poor decision today when I can make a better one tomorrow? The second is setting simple rules that remove the need to make decisions: if a trip is fewer than three nights, I don’t unpack. One less decision. Another relief is refusing to revisit decisions unless there is new material information – ruminating on decisions already made is not useful to the mind. Learn what you need to learn, and move on.

Gratitude

In these small ways, I have found a little more space to get bored. To look out of the window in the airplane and stare at clouds. To be in awe. To be present. But I know it is not enough. And I continue to seek.

The gap between what we ask and how we care for our brains is about to widen. Artificial intelligence will not lighten the load on our minds. It will raise it. As routine cognitive work is automated and information becomes effectively infinite, the scarce resource stops being analysis and becomes judgment, discernment, the ability to see what others miss. The intuition to ask a great question. The second and third level thinking.

None of that comes from a depleted brain. It comes from one with room to recover and connect. One which is cared for. One for which we have paused to show gratitude for its hard work.

We have grown wildly ambitious about what we ask of our mind, and strangely careless about how we care for it. The more we ask, the more it is owed. The more we rely on it, the more we have to protect it. The more we push it, the more we have to restore it.

Not because it is a muscle.

But because it is not.

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Let me start by admitting I got this wrong. For a while, I was sure the lasting legacy of the AI revolution would be the data centers themselves. Every great technological boom (and bust) seems to leave behind a physical footprint, the place where the capital deployed to magnify the bubble is actually poured.

History suggests that transformative change usually arrives this way. We did not build the automobile to save horses - we built it because horses were slow, expensive, unpredictable, and increasingly inadequate for a growing industrial society. The reduction in horse manure that once filled city streets was a side effect. Cleaner cities were the outcome, not the motivation.

Likewise, we did not build railroads to reduce the cost of transporting goods - we built them because people wanted to move faster and farther. Railway investors lost fortunes in the 1840s, but society inherited the tracks.

We did not lay fiber-optic cables beneath oceans because we dreamed of connecting humanity. We did it because investors saw enormous profits in the internet. The dot-com crash destroyed trillions of dollars in market value, but the fiber-optic networks built during the frenzy became the backbone of the internet economy. In hindsight, the infrastructure often outlasts the boom that created it. The speculation disappears, but the assets remain.

So what will remain now? Earlier, I argued that surely the AI revolution would leave behind endless rows of server farms stretching across deserts and industrial parks around the world. I assumed the enduring infrastructure of the AI age would be data centers. After all, they are the most visible manifestation of the boom. Governments have been competing to attract them. Investors have been pouring billions into them. Entire industrial parks are being redesigned around them.

Yet data centers are surprisingly ephemeral assets. Servers depreciate quickly. Chips become obsolete within years. Computing architectures evolve. Facilities are retrofitted, upgraded, repurposed, and sometimes abandoned as technology marches forward. The useful life of the intelligence inside them is often far shorter than the useful life of the buildings themselves. This was my critique a few years ago. Yet now, it seems the lasting infrastructure of the AI boom may not be data centers at all.

It may be energy. Specifically, solar.

There is, after all, a strange possibility and irony unfolding before us with AI. It seems that the technology that many fear will consume vast amounts of energy and bankrupt the world of power may end up becoming the single greatest catalyst for clean energy adoption in modern history, potentially even leading us to a carbon-neutral planet.

For decades, governments, scientists, activists, and policymakers have tried to accelerate the world’s transition toward renewable energy. The arguments were many: climate science, environmental responsibility, the need to protect future generations, and the rest we have all heard over the years.

The progress was slow (at best). Costs remained high and unattractive for businesses. Governments needed to provide incentives. Politics took us back and forth on the issue. Climate tech became important. Then it wasn’t. The science was rebutted. It was a never-ending issue.

Then the world found an appetite far stronger than its wish for a cleaner planet: Artificial Intelligence (AI).

The world is entering a period in which demand for electricity is growing faster than many grids can comfortably supply. AI models, hyperscale data centers, autonomous systems, electrified transportation, advanced manufacturing, cloud computing, and digital infrastructure are all competing for the same finite resource: electrons. Global data-center electricity consumption reached roughly 415 terawatt-hours in 2024, representing about 1.5% of all electricity consumed on Earth. More importantly, data-center electricity demand has grown at approximately 12% annually since 2017 - multiple times faster than global electricity demand.

The question is no longer whether we need more power - the question is how quickly we can build it, and how quickly that is changing.

Ten years ago, building one gigawatt of utility-scale solar capacity cost roughly $5.3 billion. Today, that figure has fallen to approximately $691 million. An 87% decline in a decade. Very few technologies in modern industrial history have experienced a cost curve this dramatic. For the same amount of capital that built one gigawatt of solar power ten years ago, the world can now build nearly eight.

Whereas semiconductors have benefited from Moore’s Law of cost and scale, solar is benefiting from something similar: Wright’s Law, also known as the Learning Curve, in which each doubling of global solar deployment has historically reduced costs by roughly 20–30%. As the world installs more solar to power AI, the technology becomes cheaper, which encourages even more deployment. Ultimately, we have two compounding curves - one computational, one energetic - which are now reinforcing one another.

The implications of this are profound.

A decade ago, $10 billion would have purchased less than two gigawatts of solar capacity. Today, the same $10 billion can buy nearly fifteen gigawatts. In the language of venture capital, few industries have ever delivered such a dramatic improvement in output per dollar invested.

And the world has responded accordingly. In 2024 alone, the world installed a record 597 gigawatts of new solar capacity, representing a 33% increase over the previous year. Global cumulative solar capacity surpassed 2.2 terawatts. Perhaps the most astonishing statistic is this: it took humanity nearly seventy years to deploy the first terawatt of solar power. It took just two years to deploy the second.

That is not linear growth – it is an industrial-scale acceleration rarely witnessed in modern history.

Solar generation itself has risen from roughly 256 terawatt-hours in 2015 to nearly 2,800 terawatt-hours in 2025 - more than a tenfold increase in a single decade. Meanwhile, more than 90% of newly commissioned renewable-energy projects globally now produce electricity at a lower cost than the cheapest new fossil-fuel alternatives. What once required subsidies increasingly competes on economics alone.

So why solar?

Not to save the environment. Not because of carbon tax credits. Not to protect future generations. But because it is one of the fastest ways to get more energy to feed the greedy AI machine.

Never mind that the cost has drastically decreased. The more important variable is time. The energy AI demands needs to be produced today. Not ten years from now. AI doesn’t care what energy source is best. AI cares what energy source can be built fast enough.

And most energy infrastructure is notoriously slow to build. Natural-gas infrastructure can take five years to permit, finance, and connect to the grid. Hydroelectric and nuclear plants take even longer, a decade or more from planning to commissioning.

Solar and wind are different. A utility-scale solar farm can often move to operation in as little as 20 months, and in some places faster. Onshore wind is not far behind, at roughly two to five years. Both are modular, scalable, and relatively simple to deploy compared with almost every other major source of generation, and solar in particular can be sited and switched on faster than anything else on the grid.

That distinction matters enormously.

Whereas AI operates on software and venture capital timescales, energy infrastructure has historically operated on industrial and sovereign timescales. Solar and wind are two of the few energy technologies that can partially bridge that gap.

If speed is the deciding factor, wind deserves a place in this story too. On cost, though, wind comes second, and the reason is the learning curve: solar’s falls about 20 to 24% with every doubling of deployment, against roughly 15% for wind, because solar panels are stamped out in factories while every wind farm is a civil-engineering project fitted to its own site. The AI buildout will narrow that gap, and wind keeps a real edge in that it often blows hardest at night and in winter when solar fades, making the two complements rather than rivals. But the gap is structural, not just a matter of volume, keeping solar ahead. For the fastest, cheapest electrons the AI race can buy, solar is still the answer.

History is full of technologies that won not because they were theoretically superior, but because they arrived when they were needed. The great irony here is that humanity’s race to build AI, an intensely energy-consuming technology, may become one of the largest accelerators of renewable-energy deployment ever witnessed.

The same economic forces driving trillion-dollar investments in AI are simultaneously driving unprecedented investments in renewable energy infrastructure. The race to build smarter machines is becoming the race to build more electricity. And the fastest way to build that electricity increasingly happens to be solar.

The great irony of the AI age may be that our hunger for AI ultimately accelerated humanity’s path to carbon neutrality.

Humanity’s pursuit of ever-greater computational power - our desire for faster models, larger systems, more automation and more productivity - may accomplish what decades of environmental advocacy struggled to achieve on its own.

Future generations may look back at the AI boom the way we look back at the railway boom or the fiber-optic buildout. They may remember the speculation, the exuberance, and the fortunes won and lost. But what they inherit may be something entirely different from what investors thought they were buying.

They may inherit a world covered in abundant, inexpensive sunlight.

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The sky felt ancient. Comforting. Fixed, silent, untouched by us. For millennia, we looked to it for guidance. Our birthdays were related to it, giving us our horoscopes and personalities, some would argue. Our life directions came from it, some cultures would predict.

Occasionally, a star would move and catch my attention. If it stayed still long enough, I assumed it belonged to the universe rather than to industry, and usually I was right. That made me smile.

The year was 1987.

Today, I catch myself pausing before I point at anything. Is that Venus, or is it Starlink? A constellation, or a communications network owned by a private company?

The sky itself has changed. Artificial light at night increased globally by at least 49% between 1992 and 2017, while citizen-science observations suggest the visible night sky has been brightening by almost 10% a year in many places. Above that brighter Earth, orbit has become crowded too: there are now more than 12,000 active payloads circling the planet, including thousands of satellites belonging to a single private company.

What used to feel distant and scientific now feels commercial and strangely familiar. That is the clearest sign of what is actually happening: the real story about space today is not exploration. It is infrastructure.

Which raises the question worth asking. Why has capital suddenly become obsessed with orbit, and what is everyone reading the headlines still getting wrong about it?

I. The Real Space Economy Points Back at Earth

Most people still think about space through the lens of science fiction. Rockets. Astronauts. Moon missions. Mars colonies. But that is increasingly the wrong framework.

Roughly 4,400 satellites reached orbit in 2025 alone, more than in any previous year in history, and roughly 65% more than the year before. Yet almost none of them were going anywhere near the moon.

Instead, they are being deployed to build a new infrastructure layer around Earth itself: one focused on communications, observation, navigation, climate monitoring, defense, logistics, agriculture, and increasingly, real-time machine intelligence.

Within the next five years, satellite constellations are expected to deliver broadband connectivity directly to handheld devices almost anywhere on Earth, including to billions of people who have never had reliable internet access. SpaceX’s Starlink ended 2025 with approximately 9 million subscribers, quietly becoming one of the fastest-scaling internet providers in history.

The scale of this is easy to underestimate. The global space economy reached a record $613 billion in 2024, and the commercial sector accounted for 78% of it, with government budgets making up the remaining 22%. Most of that value comes not from launches but from the commercial layer being wrapped around the planet.

Farmers in India use satellite imagery to optimise irrigation and fertiliser dosing. Fisheries use orbital monitoring to identify sustainable catch zones. Insurance companies increasingly rely on real-time Earth observation to underwrite climate risk. Banking systems, shipping routes, military operations, weather forecasting, ride-sharing apps, and even ATM timing synchronisation now depend on satellites functioning invisibly in the background.

The real buildout is not happening outward into the galaxy, it is happening around the planet. This is why the sector matters: not because humans may eventually live on Mars, but because life on Earth is increasingly being run from orbit.

That distinction matters: it mirrors a pattern we have seen before.

In the early days of the internet, most people thought the story was websites and email. In reality, the transformational shift was the creation of a global communications backbone that would later reshape commerce, finance, media, logistics, and daily life itself.

Investors are beginning to realize that orbit is becoming what railroads were to the industrial era, what electricity grids were to the twentieth century, and what cloud infrastructure became to the internet economy: a foundational layer upon which entirely new industries are built.

Space increasingly looks like the next version of that story. Only this time, the infrastructure layer sits above the planet rather than across it.

II. The Cost Curve Changed Everything

The fascination around SpaceX is not really about rockets either. It is about what the company represents economically.

Historically, launch companies were government contractors dependent on state budgets and political cycles. SpaceX changed the model entirely by making launch commercially scalable, and in doing so, it dramatically altered the economics of access to orbit.

In the 1980s, putting one kilogram of cargo into low Earth orbit aboard the Space Shuttle cost approximately $54,500. Today, SpaceX’s Falcon 9 charges closer to $2,940 per kilogram because the company learned to land and reuse its boosters rather than discard them after every launch.

That is not just engineering progress. It is the economic trigger point for an entirely new industry.

Every industrial revolution begins when a critical input becomes radically cheaper: bandwidth, compute, electricity, solar energy.

Orbit is now undergoing the same cost collapse, and once the cost curve breaks, entirely new business models emerge almost overnight. That is why investors care.

Because SpaceX is not simply a rocket company. It is simultaneously a launch provider, a satellite network operator, a defense infrastructure player, a communications backbone, a manufacturing ecosystem, and potentially one of the largest data-distribution systems ever built.

Starlink alone reportedly ended 2025 with roughly 9 million subscribers. Some projections suggest it could exceed 16 million by the end of 2026. That means investors are no longer valuing SpaceX as aerospace. They are beginning to value it more like infrastructure, or even like the early internet itself.

III. Space has Quietly Become a Geopolitical Layer

For most of the last half-century, space was framed as spectacle.

Flags planted into lunar dust. Grainy television broadcasts. Rival superpowers racing toward symbolic milestones that ordinary people could point to and understand. The United States versus the Soviet Union. Later, more loosely, the United States versus Russia. Everyone else watched from below.

But something subtle has changed over the last decade. Space is no longer primarily a prestige project.

It is becoming a dependency, and dependencies change how nations behave.

Today, an orbit is less a scientific frontier than a strategic operating layer wrapped around the planet itself. What matters is no longer who reaches space first, but who controls the infrastructure once they get there: the launch systems, the satellite constellations, the communications networks, the supply chains, the manufacturing capabilities, the data flows, and ultimately, the economic leverage that sits on top of them.

The United States still dominates commercially through SpaceX and the ecosystem growing around it. But China is building parallel capabilities at extraordinary speed. India, Japan, Korea, Europe, and the UAE are all accelerating their own sovereign space strategies, not because space has become fashionable, but because it has become foundational.

Governments increasingly understand that communications, navigation, banking systems, logistics, agriculture, weather forecasting, defense operations, and AI-enabled decision-making now depend on orbital infrastructure functioning continuously in the background.

That realization changes the psychology entirely.

Because once something becomes infrastructure, nobody wants to depend entirely on somebody else’s version of it.

The logic begins to resemble every other strategic chokepoint humanity has fought over before: oil, semiconductors, electricity grids, rare earth minerals, fiber-optic cables. Space has quietly joined that list. And like every previous chokepoint, dependency suddenly matters during moments of crisis.

No country wants to discover that its communications, mapping systems, or climate intelligence depend entirely on somebody else’s infrastructure when the world becomes unstable. Sovereign space capacity is beginning to resemble sovereign energy capacity: not optional, but foundational.

Perhaps that is the deeper reason private capital has become so fascinated by the sector. Investors are not simply betting on rockets, they are betting that orbit becomes one of the defining control layers of the twenty-first century.

IV. The Investment Story is Much Bigger than Rockets

The visible companies attract the headlines.

Rocket Lab is already public. SpaceX is widely expected to become one of the defining IPOs of this generation. The Exploration Company is building reusable cargo capsules designed for low Earth orbit.

But history suggests the largest fortunes during technological shifts are rarely created only by the most visible frontier companies.

During the California Gold Rush, many of the most durable fortunes came not from mining gold itself, but from selling the picks, shovels, denim, railways, and infrastructure surrounding the rush.

The same pattern emerged during the internet era. The visible winners were websites and apps. The deeper value accumulated in cloud infrastructure, semiconductors, broadband networks, server farms, and the invisible architecture powering the system beneath the surface.

Space increasingly appears to be following the same structure.

Above the launch layer sits the application economy: climate intelligence, agricultural optimization, insurance underwriting, telecommunications, defense systems, mobility, logistics, and AI-driven geospatial analytics, all built on orbital networks.

Below it sits the industrial stack: propulsion systems, optics, semiconductors, sensors, advanced materials, batteries, gallium, lithium, rare earth minerals, and the same strategic dependencies already reshaping the AI race.

A satellite, ultimately, is a chip wrapped in metal pointed back at Earth.

The closer you look, the clearer the convergence becomes.

AI depends on semiconductors, semiconductors depend on minerals, and space depends on both.

Which means the industries shaping the next decade are no longer evolving separately. They are beginning to stack on top of one another into a single interconnected infrastructure system.

That may be the deeper reason investors are paying attention now: once infrastructure layers converge, economic outcomes tend to be enormous.

Railroads transformed industry, electricity transformed cities, the internet transformed information, and artificial intelligence is transforming cognition.

And space increasingly looks like the layer connecting all of them together from above.

V. The Question That Actually Matters

The most interesting question may no longer be whether humanity reaches Mars. It may be whether we fully understand what is already happening above our heads.

Because this pattern feels historically familiar. First comes infrastructure, then applications, then dependency, then transformation.

Most people only recognize the shift once the final stage arrives. When railroads emerged, people saw trains. When the internet emerged, people saw websites. Today, most people still see rockets.

Which may be why most people still underestimate it.

The deeper shift is that humanity is quietly wrapping the planet in a programmable economic layer capable of transmitting connectivity, intelligence, observation, coordination, and eventually autonomous decision-making at a planetary scale.

Like every infrastructure revolution before it, the biggest changes may feel invisible at first.

Until suddenly they are everywhere.

Which perhaps brings us back to the night sky itself.

The most revealing thing about space today is not that we are looking farther away. It is that the layer above us has become so dense, so commercial, and so economically important that we now pause before deciding whether the light we are admiring belongs to the universe, or to us.

That inversion has entirely reframed how I have been thinking about the sector.

Earlier this month, at a board meeting of the Karman Project, where I have served for the past four years alongside people who have spent their careers inside the global space industry, I asked what felt like a deliberately basic question: What is the average person still fundamentally misunderstanding about space?

The answer came back almost immediately.

Space is no longer mainly about leaving Earth. It is increasingly about running it.

A farmer’s strength determined how much land he could work, and a laborer’s endurance determined how long he could produce. Survival and economic output were physically intertwined. The body was not separate from the economy. It was the economy.

Then the Industrial Revolution arrived. Machines began replacing physical labor as engines amplified strength and factories mechanized production. Over time, as machines could clearly outperform humans in physical abilities, society no longer needed the human body in the same way it once did, and economic value gradually shifted from muscle to cognition.

And so humanity underwent a civilizational shift, from body-based productivity to mind-based productivity. The modern economy is powered not by physical stamina but by intellectual capacity: analysis, strategy, software, finance, communication, decision-making. Today, the most economically valuable work in the world happens with barely any movement at all. Most of us spend our days sitting in front of screens, generating output entirely with our minds, and we have come to think of this as normal. It is, historically, very strange.

But here is the part that most narratives of progress skip. Industrialization did not just liberate the body; it created a new form of extraction entirely. The knowledge economy runs on attention the way the agricultural economy once ran on muscle.

We no longer exhaust the body in the same way; we now exhaust the mind.

This is the part worth sitting with, because almost everything we call “burnout” is a symptom of it.

Knowledge workers are now interrupted every two minutes during core work hours, roughly 275 times a day, by meetings, emails, or chats. Meetings after 8 p.m. are up 16% year over year. The average worker receives 117 emails a day and more than 50 messages outside core hours. And of the time we do have, 57% goes to communicating in meetings, email, and chat, with only 43% spent actually creating.

In other words, much of modern work is no longer the production of output. It is the management of information surrounding output.

The nervous system is responding. 64% of knowledge workers say technology has negatively affected their lives in the past year, and 43% report stress and anxiety from notification overload and constant platform switching.

The modern worker may not come home physically broken in the way a factory worker once did, but many arrive mentally fragmented, emotionally depleted, and cognitively overloaded.

Burnout became the new physical fatigue.

And in many ways, modern wellness culture emerged as a response to this cognitive overextraction. Most of us have tried some version of it: meditation, mindfulness, digital detoxes, breathwork, sleep optimization, nervous system regulation. Some of it has helped. None of it has solved the underlying problem.

What gyms became for bodies after industrialization, mindfulness practices became for minds after digitization.

Just as earlier generations eventually had to reintroduce movement into lives made sedentary by machines, modern humans increasingly need to reintroduce stillness into lives overwhelmed by cognitive demand. And yet, none of this resolves the underlying problem. It only makes it bearable. Gyms did not solve the problem of sedentary work; they made it survivable. The same holds for mindfulness in the cognitive economy. It treats the symptoms of extraction without changing what is being extracted.

And now AI may be shifting the model entirely – perhaps it could even be argued that we have brought our minds and intellectual capacities to a breaking point and this change is now an imperative.

Because if the Industrial Revolution automated the body, the AI Revolution is beginning to automate the mind. Large portions of cognitive labor, including writing, coding, summarizing, analyzing, researching, organizing information, and even aspects of creativity, are increasingly being absorbed by intelligent systems. And adoption is moving faster than any prior workplace technology: 75% of knowledge workers are already using AI at work, with usage nearly doubling in six months.

History suggests something happens every time technology removes humanity from one layer of production: human value migrates upward into another layer. When machines surpassed physical strength, we moved toward cognition. So if AI begins to surpass cognition, where do we go next?

Is “spirit” really what comes next? Or is that just the most flattering story the mind economy can tell itself about its own obsolescence? The body-to-mind transition has only happened once in human history, and one pattern is not a law. So what if value does not move upward at all, but sideways, into capacities we do not yet have a vocabulary for? And if it does move toward something like spirit, or presence, or meaning, what does that actually look like on a Tuesday afternoon?

I am not entirely certain. But a few things do seem to be true. As intelligence becomes abundant, other qualities become scarce. When everyone has access to infinite information, discernment matters more. When AI can generate endless content, authenticity matters more. When machines can imitate analysis, meaning becomes more valuable than knowledge alone. When productivity becomes automated, presence becomes differentiated. When the cost of producing anything collapses, the cost of knowing what is worth producing rises.

Call this whatever you want: spirit, in the non-religious sense, or taste, or judgment, or the capacity to remain conscious in the middle of infinite stimulation. These are not soft skills. They are the inputs that decide what AI gets pointed at, how its outputs are filtered, and whether what gets made means anything when it lands.

Perhaps this sounds abstract today only because we are standing too early in the transition to fully comprehend it.

A farmer 150 years ago could not have imagined a computer interface, let alone how a human being might generate wealth through spreadsheets, software, investment banking, venture capital, or brand strategy sitting behind a desk next to thousands of other people. The idea that someone could sit in a tall glass tower (which was unfathomable to build and beyond his ability to imagine), in front of a glowing screen (which clearly could not make sense to him), away from fresh air all day, moving symbols, and generating more economic output than an entire field of laborers would have seemed incomprehensible. Why? Because he understood productivity through the dominant paradigm of his era: the body. That was a mere 150 years ago.

In the same way, we may still be trapped inside the paradigm of the mind. We assume intelligence, analysis, and cognitive processing are the ultimate forms of value because they define the current age. But future generations may view this the way we now view manual agricultural labor: necessary for a period of history, but ultimately transitional and only necessary until the right machines are invented.

One hundred and fifty years from now, humans may look back at our era and see it as the final age of purely cognitive capitalism. And they may build economies around capacities we will need to start developing now: the ability to direct collective meaning, to cultivate emotional stability, to create trust in synthetic worlds, to regulate attention, to embody wisdom, to integrate technology without losing humanity, and to bring coherence to increasingly intelligent systems.

The future economic unit may not simply be labor or knowledge, but human depth itself.

Because every technological revolution eventually forces humanity to ask the same question: if machines can increasingly do what humans once did, what remains uniquely human?

The Industrial Revolution asked this of the body, and the AI Revolution may ask it of the mind. Perhaps what remains, after muscle is automated and cognition is augmented, is something older, deeper, and harder to replicate: the human spirit.

So what are you protecting in yourself that a machine cannot replicate? And what would it take to make that the most valuable thing you do? How can each of us lean into the elements of our spirit that make us most human, and re-ignite that power within us as we look, once again, to a new world?

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That detour taught me something I keep returning to: the search for purpose almost never begins where we think it does. What follows is what I have come to believe about that search, and why I think most of us are looking in the wrong place first.

We live in an age obsessed with purpose. From Gen Zs to mid-lifers, everybody seems to be searching for deeper meaning in life and work. A recent global survey of more than 23,000 young adults found that 89% of Gen Zs and 92% of millennials consider a sense of purpose central to their job satisfaction and well-being.

Even mid-career professionals are rethinking the prestigious, linear career path. New Possible’s 2026 UK workforce survey found that employees are looking for greater freedom, purpose and enjoyment in their work, with 38% planning to job-hunt this year. A third of 45- to 54-year-olds expect to change career before retirement. The hunger for meaning is no longer a generational story. It is the story.

Where does everyone look for it? Podcasts. Retreats. Strategy sessions. Personality tests. Career pivots. Endless introspection. We want to begin at the top of the mountain. Clarity first, calling first, spiritual alignment first.

But purpose is rarely discovered from the top down.

What if I told you that the path to purpose often starts with a pair of dumbbells?

I have come to believe that human energy is layered, and that we ignore the layering at our peril. Performance psychologists, elite coaches, and leadership thinkers have understood this for years. I first read The Making of a Corporate Athlete by Jim Loehr and Tony Schwartz almost 25 years ago, and the argument only resonates more as time goes on. Their claim was that executives, like elite athletes, must train systematically across four levels of capacity to sustain high performance: the physical body as the foundation of energy, building upward through emotional, mental, and spiritual capacity.

More recently, Ricardo Sunderland’s The Energy Advantage extended this into a contemporary leadership context, arguing that physical and mental energy alone are no longer enough for a thriving, fulfilling life. Emotional and spiritual energy are equally essential, and it is all integrated.

My working theory – observed across founders I back, friends, myself - is that there are four forms of energy that shape the quality of our lives: physical, mental, emotional, spiritual.

The mistake modern life encourages is trying to live from the top while neglecting the bottom. People want spiritual clarity while sleeping badly. They want emotional resilience while being sedentary. They want mental sharpness while under-recovered. They want purpose while physically depleted.

But it is difficult to access higher-order states when the foundation is unstable.

A tired body makes a noisy mind. A noisy mind creates emotional volatility. Emotional volatility clouds meaning.

Which is why the path upward often begins with something unexpectedly ordinary: movement, exercise, physical well-being.

When you lift weights, walk briskly, sprint, climb, row, stretch, or train with intention, you are not only changing your body. You are restoring the first layer of the pyramid. Physical energy is more than aesthetics. It is vitality. Hormonal balance. Sleep quality. Posture. Stamina. Confidence. Capacity. It is the biological platform from which every higher function becomes easier.

Once the body becomes stronger, the mind often follows. Mental energy is the ability to focus, prioritize, solve problems, hold attention, and think clearly. It is no coincidence that many people report their best ideas after exercise. Movement clears cognitive residue, reduces internal friction, and sharpens executive function.

Then comes emotional energy. This is the capacity to regulate mood, sustain optimism, handle setbacks, stay connected,and remain steady under pressure. Exercise has long been one of the most reliable interventions for anxiety, stress, and low mood, not because it solves life, but because it increases our ability to meet life.

Only then do most of us begin to access spiritual energy. This does not necessarily mean religion, though for some it can. It means meaning. Values. Direction. Service. Coherence between what you do daily and what you believe deeply. It is the feeling that your life is pointed somewhere worthwhile.

And this is where the body returns again. Because to connect with the body is to connect with the breath. Breath is the bridge between physiology and presence. It is both automatic and conscious, mechanical and sacred. For thousands of years, contemplative traditions have taught that awareness of breath is awareness of being itself, the simplest doorway back to the present moment. When we move, sweat, strain, and breathe deeply, we are not merely exercising. We are returning to essence. To aliveness. To the fact that before we are titles, roles, or ambitions, we are beings.

Purpose is often spoken about as if it appears in a flash of insight. More commonly, it emerges when the system is healthy enough to perceive it.

When your body has energy, your mind has space. When your mind has space, your emotions settle. When your emotions settle, you can hear what matters.

As an investor, I often rely on data, analysis, market potential, financial models. But I have learned to trust intuition just as much. And what I have noticed, over time, is that intuition is quieter than we think. It does not shout. It tends to surface when the body is well, when I am sleeping, moving, and breathing properly. When I am depleted, I hear it less clearly, and I am more likely to mistake noise for signal.

The lesson is not that everyone needs dumbbells specifically. The lesson is that purpose rarely begins where we think it does. It begins lower in the pyramid than we want it to: with the body, with the breath, with movement, with presence.

So if life feels foggy, do not begin by asking what is my purpose? Begin lower. Strengthen the base of the pyramid. Move. Breathe. Sleep. Show up to your body before you ask anything of your soul.

Because the path to purpose, in the end, does not begin with revelation. It begins quietly, in the body, with connection, repetition, and the willingness to keep showing up.

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In the 1840s and 1850s, hundreds of thousands of people flooded into California chasing gold. Most of them did not find it. The real winners were the merchants and suppliers who understood what the diggers needed: picks, pans, shovels, food, transport, and workwear. Samuel Brannan saw the rush coming before most and positioned himself to meet it, stocking his store with every mining supply he could find before the crowds arrived. Levi Strauss arrived not with a claim but with sturdy workwear for the people who did, and built a company that still exists today.

The strategy even acquired a name: the pick-and-shovel play. Back the suppliers to a boom, not the boom itself. Sell the tools, not the gold.

The pattern has repeated in every technological wave since. During the railroad boom, it was the steel and timber suppliers. During the internet boom, it was the server manufacturers and fiber cable layers. The visible opportunity draws the crowd. The underlying system builds the wealth.

We are in a gold rush right now. Only this time the gold is intelligence. Artificial General Intelligence (AGI) is an active race, with the world’s largest companies, most powerful governments, and most ambitious investors all chasing the same finish line. Almost nobody is asking the prior question: what does getting there actually require? In physical terms, material terms, in-the-ground terms, the answer looks a lot like a mine.

The Race Has a Physical Cost

AI is consuming compute at a pace that efficiency gains cannot keep up with. Think of it this way: in 2020, asking an AI a question took roughly the same computing effort as sending an email. Today, a single exchange with a model like GPT-4 is closer to streaming an hour of video. By 2030, one AI conversation could require the computing equivalent of downloading an entire library. The infrastructure required to run this is scaling accordingly. Hyperscalers spent $92 billion on data center capital expenditure in 2021; by 2025 that figure reached an estimated $415 billion - more than the GDP of the UAE. Each AI server rack contains 200 to 300 kilograms of copper. A single hyperscale data center can require up to 50,000 tonnes. Enough to wire every home in a city the size of Dubai. The demand is accelerating. The materials required to meet it are not.

Efficiency gains are real. The inference cost for a system performing at GPT-3.5 level dropped 280-fold between November 2022 and October 2024. Moore’s law in action. Demand, however, is rising fifty times faster than efficiency is improving, absorbing every gain and compounding further. This is Jevons Paradox at industrial scale: the more efficient intelligence becomes, the more of it the world consumes. The US Geological Survey’s list of minerals deemed essential to national security and economic stability yet vulnerable to supply disruption, built by modeling more than 1,200 supply disruption scenarios, grew from 35 in 2018 to 60 in 2025, driven by AI, defense, and industrial demand. Every technology stack that matters depends on it.

The obvious counterargument is quantum computing, a fundamentally different type of computer that, in theory, could solve in seconds problems that today’s machines would take years to crack. If quantum delivers on its promise, the compute constraint dissolves and the materials question becomes less urgent. The problem is timing. IBM has revised its roadmap from one million qubits by 2030 to 100,000 by 2033. Jensen Huang, CEO of Nvidia, told attendees at CES 2025 that practical quantum computing is fifteen to thirty years away. So we have a substantial gap: the materials deficits arrive by 2035. Quantum arrives, on the most optimistic projections, sometime after that. In the window that matters for the intelligence race, existing chips must evolve, and evolving chips means better materials.

The Constraint Is Already Here

Every leap in computing performance across the past seventy years has been driven by a change in material: germanium to silicon, silicon to gallium arsenide, then to gallium nitride and silicon carbide, and now toward gallium oxide and aluminum nitride. All are materials I was, until recently, very unfamiliar with.

Each generation of chips required not just better engineering but fundamentally different raw materials. The next generation is no different, and the materials it requires are in shorter supply than any before it. The next generation of chips, capable of sustaining AGI-level compute, requires materials that are rarer, harder to refine, and more geographically concentrated than anything the industry has depended on before.

China spent three decades building dominance over the minerals from which chips are made. By 2024, that effort had produced control over 98% of global gallium production and the majority of refining capacity across the chip basket. In December 2024, it banned gallium exports to the United States. That is one chokepoint already in place.

The IEA’s Global Critical Minerals Outlook 2025 is unambiguous: copper faces a 30% supply shortfall by 2035, lithium a 40% deficit, based on the current pipeline of announced projects. The supply side is already deteriorating. Chile, which produces a quarter of the world’s mined copper, posted its lowest monthly output in nine years in February 2026, driven by nothing more than declining ore grades. New copper discoveries have fallen 80% since 2010.

Here is the part that does not get enough attention: according to S&P Global, the average time from discovery to production for a mine coming online today is approximately 18 years. A child born today will finish university before a mine discovered this year produces its first tonne. A mine needed by 2035 had to be discovered in 2017. Many were not. Higher prices cannot compress that cycle. This isn’t something the market will sort out over time. The constraint is physical, and it’s already there, built into the system

And while that clock has been running, private capital has been looking the other way. PE and VC deal count in metals and mining fell from 124 in 2023 to 59 in 2024, with aggregate transaction value collapsing by 55%. The IEA estimates $590 billion in new investment is required by 2040. The capital is not there. And every year it is not there, the window to close the gap gets smaller.

Capital Is Looking the Other Way

With every crisis, or looming one, comes an opportunity. But we can’t know which of the materials is the one the next chips will need.

Investment growth in critical mineral development slowed to just 5% in 2024, down from 14% the year before, with real growth after inflation at 2%. The industry that makes everything else possible is receiving almost none of the capital that will be required to keep it running. Where has that capital gone? It has found its way to the layer above, the models, the data centers, the chips, while the layer beneath remains largely unfunded.

But this is starting to change. In early 2025, KoBold Metals, an AI-driven mineral exploration company, raised $537 million in Series C funding from Andreessen Horowitz and Bill Gates’ Breakthrough Energy Ventures, reaching a valuation close to $3 billion. While this is just a signal and not yet a trend, investments like this from such major players often mark the start of something bigger.

Instead of betting on which materials are going to be radically important in the next generation of chips, or in which mining projects could win or discover them, the third order thinking could take us to an opportunity to invest in the technologies that are being created to optimize these tools.

The New Picks and Shovels

Today, mining is a full-stack industrial system spanning exploration, extraction, processing, logistics, and recycling. Across that system, six technology categories are emerging as the most consequential opportunities.

• Data and AI for exploration speed up the discovery process, which is crucial since new finds have dropped 80% in the past fifteen years. Companies like KoBold use machine learning to find deposits that traditional geology has missed, focusing on the start of a supply chain that can’t afford to slow down.

• Robotics, mobility, and hardware help solve the problem of declining ore quality. They make it possible to extract minerals from deposits that were once too costly or unsafe to mine. As ore grades drop worldwide, these technologies are key to keeping supply steady.

• Recycling and recovery offer a secondary supply of minerals without needing new mines. The IEA says that expanding recycling could cut the need for new mining by 25 to 40% by 2050. For minerals used in chips, where the US relies entirely on imports and one country controls refining, secondary supply is just as important strategically as it is economically.

• Energy generation and storage help power remote mining sites efficiently and sustainably. These factors are now often key to getting permits and community approval for new projects.

• Decarbonization technologies now often decide whether a project moves forward or gets delayed. As regulations get stricter and communities pay closer attention to environmental impact, having a real plan to cut emissions is becoming a must-have, not just a nice-to-have.

• Synthetic biology is still far from being widely used, but it has huge potential. It could create new ways to extract minerals using biological processes, even from low-grade sources, waste, or places that traditional methods can’t reach.

Most of these technologies are now beyond the experimental stage and are being used commercially. When proven technology meets limited capital, that’s often where the best investment opportunities appear.

The intelligence rush is following the same pattern as every boom before it. The visible layer is capturing attention, capital, and imagination. The underlying system, the materials that make all of it physical, is receiving almost none of it.

Now, the questions worth asking are shifting. Will the capital chasing AI find its way to the materials that make AI possible? Will industries begin to treat minerals with the same urgency as energy? And will the next wave of innovation be defined not by what we build, but by whether we can source what it takes to build it?

As we race to super-intelligence, is the best place to invest in the technology that enables us to best extract what the earth has held for billions of years?

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Another layer of work gone, almost silently, into the ether. Not replaced or transformed, but simply removed.

I graduated university in 2001 into a world that, in hindsight, felt far more certain than we realized at the time. There was a path - narrow and demanding, but clear. Consulting or investment banking. You worked, you performed, and every couple of years you moved up or you were out. It was structured, predictable, almost linear. Like most people, I assumed disruption was something that happened elsewhere, to other industries, to other people.

Then, gradually, that certainty began to fracture. Accountants saw parts of their roles absorbed by software. Other professions followed. Tasks were automated, layers removed, entire functions redefined. What emerged was not collapse, but divergence - a K-shaped trajectory, where a small group moved upward, specializing and compounding value, while a much larger base was slowly hollowed out. Careers that once felt stable, even prestigious, began to thin.

What is happening now, however, feels fundamentally different. This time, it is not only jobs that are disappearing, but also the work previously used as training grounds and skill development for the corporate world.

At firms like Goldman Sachs and McKinsey & Company, AI can already complete a meaningful share of first-year analyst tasks. Junior legal work - once repetitive, billable, and essential for training - is increasingly automated. In software, tools such as GitHub Copilot dramatically accelerate output, but in doing so, remove many of the entry-level tasks where developers once built their foundations.

The first rung of the ladder is disappearing.

And with it, something more subtle and more consequential. We are entering a paradox in which experience is required, but the pathways to gain it are disappearing. The scaffolding that once taught people how to think, decide, and build judgment over time is beginning to erode.

The data is already beginning to reflect this. Recent figures show UK graduate unemployment at 5.7%, near post-pandemic highs. At the same time, in the first few months of 2026 alone, more than 45,000 technology jobs have been eliminated globally, with a meaningful share directly attributed to AI-driven restructuring.

This is not a marginal shift. It is a structural one. The generation expected to thrive in an AI-enabled world is, in many cases, struggling to enter it.

At the same time, the economics of work itself are shifting. It is not just that jobs are being replaced; entire categories of tasks are being repriced toward zero. Writing, analysis, coding, financial modelling—what once took hours, or entire teams, can now be done in minutes, sometimes seconds. In practice, this rarely results in zero jobs, but it does mean fewer people doing more. A team of five becomes a team of three, sometimes two. Across industries - from finance to media to medicine - the pattern is repeating.

At scale, the implications become difficult to ignore. Goldman Sachs estimates that AI could impact up to 300 million full-time jobs globally. In banking, hundreds of thousands of roles in Europe alone may disappear by 2030 as routine, process-heavy work is absorbed. Tools like JPMorgan Chase’s COIN already automate hundreds of thousands of hours of legal and contract review annually. Replicated across organisations, even modest gains in efficiency translate into entire layers of roles that are no longer created—or replaced.

But the deeper shift is not only about scale; it is about structure. AI expands the output of those who remain. Managers can oversee more, individuals can produce more, and the span of control widens accordingly. Teams shrink. At the same time, the gap between those who can effectively leverage these tools and those who cannot begins to widen, not narrow. AI does not level the playing field; it amplifies those who already know how to think.

For those starting their careers today, the implications are profound. The traditional path - education, entry-level role, progression - no longer guarantees anything resembling financial security. The intellectual economy is undergoing its own K-shaped reset.

But the more interesting question lies beyond the labor market.

Because this layer of white-collar work does not exist in isolation. It sits at the center of the modern economic system.

If large parts of the white-collar workforce begin to contract - if fewer analysts, associates, and junior professionals are earning, spending, and progressing - what happens to everything built around that spending?

Who pays for the restaurants, the travel, the discretionary services that underpin large parts of the economy? Who hires the plumbers, renovates homes, and takes holidays?

The service economy does not exist in isolation. It is downstream of income.

And here, a further tension begins to emerge.

If the future points toward smaller, AI-augmented teams - or even the possibility (and current reality) of “solo unicorns” - then wealth does not disappear. It concentrates. A handful of individuals or very small teams may generate outsized economic value, reinforcing the upward side of the K-shaped economy.

But concentrated wealth does not necessarily translate into broad-based demand. A single founder, no matter how successful, does not consume at the level of thousands of employees. They do not hire proportionally more plumbers, take exponentially more holidays, or sustain entire layers of the service economy on their own.

In that sense, the system begins to decouple. Value creation continues - perhaps even accelerates - but the distribution of income, and therefore demand, narrows.

And beyond that, something even more foundational comes into view.

The same white-collar workforce that drives consumption is also the backbone of the tax base in modern economies. In the UK, the top 10% of earners contribute over 60% of all income tax, while the top 1% alone contribute close to 30%. A relatively small, predominantly knowledge-based segment of the workforce is not only driving demand, but funding the state itself.

Those revenues underpin the systems we rarely think about until they are stressed: schools, healthcare, transport, and emergency services.

If that base begins to shrink due to job elimination - or concentrate into fewer hands - the question is no longer just about jobs or consumption.

It becomes a question of fiscal stability.

What happens to all the layers that depend on this layer?
What happens to tax revenue?
What happens to the economy as we know it?

Is this simply a more extreme version of a K-shaped economy - or the beginning of something more systemic?

A structure in which value continues to be created, but no longer distributes in a way that sustains the system built around it?

Because this may be the real shift underway.

The first wave of automation replaced manual labor.
This wave is replacing cognitive scaffolding - the layers of work that taught people how to think, decide, and build judgment over time.

And if that scaffolding disappears, we are left with a generation that must learn to build capability without the structures that once built it for them - and an older generation that must unlearn what it once believed to be true.

So I wonder – are only the jobs disappearing, or also the cognitive structures and the fiscal system that underpinned them?

Into the ether it seems they go.

One of many thoughts on my mind… so much to explore together…

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This past month, the Strait of Hormuz has fallen silent. What was once a steady stream of vessels is now a trickle. Oil prices have exceeded $100 a barrel. Fertilizer prices have doubled, with nitrogen fertilizers reaching around $700 per metric ton. LNG prices in Europe and Asia have more than doubled since the conflict began, exceeding $20 per million BTU, a cost that most emerging economies simply cannot absorb. For over a month, the world has been assessing and managing the risk of that choice - of having some of the world’s most important resources pass by a narrow strait - while working to anticipate and mitigate its second- and third-order effects.

Yet, this moment feels familiar. Only six years ago, a global crisis delivered a massive supply-chain shock, exposing how deeply interconnected our supply chains, energy dependencies, and food security truly are. This is a crude reminder of the pandemic’s lessons, and of how quickly we seem to have forgotten them.

It raises a difficult question: if the world had accelerated clean energy deployment - investing in it, scaling it, and reducing reliance on fossil fuel trade routes - would this moment look different? What if we had taken the urgency of food security to a new level? If we had adopted new supply changes more quickly? The closure of the Strait of Hormuz would still matter geopolitically, but would it carry the same economic shock? The case for switching to cleaner, more locally-sourced energy has always been clear. Today, it is undeniable.

On Energy

What is especially striking now is that energy alternatives were already less expensive.

In 2024, over 91% of new renewable power projects worldwide were cheaper to build than fossil fuel options. Last year, renewables made up 85% of all new global power capacity. Since the mid-70s, solar panel costs have dropped by a factor of 500. Battery storage costs have fallen by 99% in thirty years. Solar cell efficiency has doubled from 11% to 22% in one generation. As the IEA said: “Ten years ago, solar was a romantic story - but now solar is a business.”

And solar became a business before most people realized it.

We have seen this turning point in every energy transition, and the pattern is always the same: once a new energy source becomes decisively more competitive, the shift follows - not for ideological reasons, but because it makes economic sense. The steam engine did not replace water mills out of principle, it did so because it was cheaper and could scale. Today’s clean energy companies - building grid-scale battery storage, advancing carbon capture, and investing in climate-resilient infrastructure - are following the same logic.

For the past decade, the business world has primarily treated the energy transition as an environmental issue. That was accurate but limiting: it cast clean energy as a trade-off between values and economics in nearly every budget and investment decision. But energy transitions follow a familiar pattern: gradually, then suddenly. Years of quiet progress in cost, technology, and deployment accumulate beneath the surface until a shock makes the shift impossible to overlook. That moment has arrived. Renewables are more than a climate solution: they are a tool for energy security - local, independent, and insulated from the supply disruptions we are now witnessing. This logic holds even where climate commitments are weak. Domestic clean energy cannot be blocked or weaponized in a conflict.

Across Asia, countries have been accelerating the pace of electrification. Simply increasing EV adoption could save importers over $600 billion a year on oil. This is as much about strategy as it is about sustainability. In Europe, attention has turned to Spain, where sustained investment in solar and wind has helped soften the impact of the current energy crisis. Founders across emerging markets had been addressing the absence of existing infrastructure long before the Strait closed, bringing renewable energy solutions where they had to be viable from day one. I have previously explored what this could mean for Africa, given its vast solar potential.

The question is now inevitable: why didn’t more countries move sooner and at greater speed? More than an abstract question, this is shaping where capital flows today.

On Food

This moment also elucidates that the implications extend far beyond energy itself. LNG prices and fertilizer costs may never have entered dinner-table conversation, but they have always shaped what ends up on the table itself - only now are we beginning to notice.

The Strait of Hormuz is a critical corridor for the fertilizer trade, and its closure has sent agricultural input prices surging, putting food systems in emerging markets already under strain from climate change. This global “chokepoint” moves roughly a third of the world’s seaborne fertilizers, and since it closed, urea prices have jumped by as much as 50%, with ammonia, potash, and sulfur following. Gulf states account for nearly half of global urea exports, and Iran was one of the world’s largest exporters of nitrogen fertilizers before the war. That supply has effectively stopped, at the worst possible moment, with spring planting season underway across the Northern Hemisphere and fertilizer accounting for more than a third of farmers’ operating costs for major crops like corn and wheat. A meaningful drop in yields this year would not cause famine, but it would cause food inflation, and the countries - and households - least able to absorb it are those already under the most pressure.

The world will not just find alternative fertilizer routes and move on - it will be pushed, faster than expected, toward precision agriculture, soil health technology, and climate-resilient crop systems that are less dependent on synthetic inputs. The food security crisis and the energy transition, viewed from a distance, are the same problem in different forms. One of Global Ventures’ portfolio companies, Iyris, has developed proprietary greenhouse technology that filters heat while maintaining crop productivity, reducing the energy and water inputs that volatile global markets can no longer be relied upon to supply cheaply - precisely the kind of climate-resilient food infrastructure that this moment renders essential.

The transition in food, like in energy, was always coming. It simply needed a shock to arrive all at once.  What will it take for more of us to source locally, and for the food industry to embrace technologies that enable local food production?

On Supply Chains

The same logic is reshaping supply chains. Tanker traffic through the Strait dropped by 90% within days of the conflict starting, rerouting vessels around the Cape of Good Hope and adding up to two weeks to transit times. War risk insurance premiums surged to around 5% of a vessel’s hull value, roughly five times the rate seen at the start of the conflict and up from fractions of a percentage point before the war, meaning a single crossing now costs a standard tanker around $5 million to insure.

The exposure of supply chains built on the assumption of cheap, reliable, long-haul logistics is not a new discovery, but the accumulated evidence now makes the rethink unavoidable. Additive manufacturing and on-demand digital production, which allow components to be made locally rather than shipped across oceans, are moving from an interesting alternative to an operational necessity. Another Global Ventures’ portfolio company, Immensa, has been building that alternative for years. In their model, inventory is no longer something you hold; it is something you call up. Physical warehouses give way to secure digital libraries, parts manufactured on demand from certified files through a distributed network, wherever and whenever they are needed. Lead times compress from months to days. Any certified component becomes globally accessible and locally producible. The future of manufacturing is digital, distributed, and on-demand. And it was already here before the crisis made the old model indefensible. Given that these technologies and companies exist, what will it take for their adoption to rise and for these new supply chains to become the standard?

The technology is ready, the economics work, and the founders have been building quietly for years.

We have been here before. The pandemic exposed the same fragilities - the same supply chains, the same energy dependencies, the same food security fault lines - and we called it a wake-up call. Then, gradually, we went back to sleep. What is it in human nature that not only allows us to forget, but also to downplay the need for change?

It is often said that ‘old habits die hard’ - is this the moment when preparation stops being gradual? Will it take another crisis, and another, before the “suddenly” finally arrives? Or are we, this time, actually paying attention?