The world’s stock markets are more intertwined and unpredictable than ever. As we move toward the end of 2026, record highs, emerging uncertainties, and shifting regional dynamics dominate the landscape. What forces are redrawing the map—and how should forward-looking investors respond?
Here is a look at global stock markets. Worldwide, they were worth $158 trillion in 2025, with U.S. markets accounting for nearly half.
Global Market Snapshot
The global stock market has grown significantly.
At the end of 2025, global equity markets reached a record $157.8 trillion in market capitalization—more than $25 trillion higher than the previous year.
But the more interesting story isn’t simply how much the market has grown. It’s where that growth is happening—and how concentrated global wealth has become.
The U.S. remains in a league of its own.
U.S. Dominance vs. Global Opportunity
U.S.-listed companies now represent roughly 44% of the entire global stock market, with a combined market capitalization of $68.9 trillion. That’s more than four times the size of either China or the European Union individually.
And America’s dominance has actually increased.
Back in 2012, U.S. companies represented roughly one-third of global equity market capitalization. Today, that figure is closer to half.
Much of that growth has come from the extraordinary performance of America’s technology giants. Companies such as NVIDIA, Apple, Alphabet, and Microsoft have transformed not only the U.S. market, but the global distribution of market value.
That raises an interesting question:
If the U.S. already dominates global markets this much, where is the opportunity elsewhere?
Not Like U.S. …
The rest of the map isn’t empty, just smaller and more complicated. China and the EU are essentially tied at roughly $15.5 trillion each — enormous by any historical standard, and still less than a quarter of the U.S. market. China’s scale comes bundled with geopolitical risk and a property market still working through its problems; Europe’s re-rating — equities up nearly 40% year over year — rides on unusually specific catalysts, German spending chief among them, layered on an economy still weighted toward banks and industrials rather than platforms. India, at $10.6 trillion, is the one growth story that doesn’t need much interpreting: a huge population, an expanding middle class, and a growing role in global supply chains. And across Japan, Korea, Taiwan, and Singapore, the real story isn’t consumer AI at all — it’s the chips, the manufacturing, and the infrastructure sitting underneath it.
The Bigger Picture
The most striking takeaway from the chart isn’t simply that the global stock market has reached $158 trillion.
It is that market leadership continues to concentrate around innovation.
The U.S. has increased its share of global equity value by more than 10 percentage points since 2012, largely because investors believe innovative companies are formed there.
And that creates an interesting tension for investors.
On one hand, diversification matters. No country stays dominant forever, and concentrating too heavily in any single market creates its own risks.
On the other hand, diversification for its own sake isn’t necessarily a strategy. Capital tends to flow toward places where innovation, productivity, capital formation, and economic opportunity are strongest.
The challenge is figuring out whether today’s winners are experiencing a temporary boom … or participating in a much larger structural shift.
Takeaways for Investors
The global market isn’t a static pie. It is a constantly changing map of capital, innovation, and expectations.
The U.S. currently owns the largest piece by a wide margin. But beneath that headline are several competing stories: China’s enormous but uncertain market, Europe’s potential re-rating, India’s long-term rise, and Asia’s increasingly important role in the technology supply chain.
And then there is AI.
The AI boom is already influencing where capital flows, which companies command the highest valuations, and which countries are gaining market share. If AI continues to reshape productivity and corporate profitability, today’s market leaders could become even more dominant.
But technological revolutions rarely follow a straight line.
That is why investors shouldn’t simply ask, “Where is the market today?” Instead, “What would have to change for this picture to look completely different five or ten years from now?”
The good news is that the global opportunity set is enormous … and growing.
Perhaps you can’t reliably predict the future … but that doesn’t mean you can’t prepare for it reliably.
Friday was the Cowboys’ last preseason game. We had a pretty good preseason, all things considered.
It wasn’t exactly the prettiest (partly because it was the first games of the season, but also because many of the starters sat the game out to avoid injury). With that said, it was still a fantastic experience. The NFL (and Jerry Jones) knows how to put on a show.
A photo of my two sons in front of the new AT&T Stadium in 2009
It’s Easy to Feel Good at the Start of a Season.
Lots of people ask me how the Cowboys look this year. The truth is, at this point in the season, it’s impossible to know because injuries have a dramatic impact on the game.
Regardless, each year I choose to be optimistic about the chance of a post-season run.
That kind of logic (or lack thereof) is why I think automated trading is better than humans attempting to do it themselves. It’s a way to make objective decisions and eliminate fear, greed, and discretionary mistakes.
On the other hand, it feels so good to hope!
A Lesson From the Game.
I had an interesting discussion at a game recently. My guest commented that Jerry Jones is a fantastic businessperson – which is hard to argue – but probably shouldn’t be running the team. He believes the team needs a change of pace.
While I don’t know if that’s why we tend to struggle so much more late in the season, it reminded me of a great business lesson.
Entrepreneurs often mistake their domain expertise for general expertise. “I’m fantastic because I’m fantastic at all these different things.” As a result, they overestimate their ability to be great at things outside their unique strengths. A similar issue is that many people believe they are deep thinkers because they think deeply about what they think about. However, they often don’t realize how narrow their range of thinking is, and how many things fall outside their expertise, interest, or even consideration.
We hire people to take on different roles, because they free us up to focus on what we’re truly experts in. It’s also why AI is so powerful.
Learning to offload tasks that you may not be as fantastic at as others is a great way to free up time to focus on not only the things that you’re great at – but also bring you joy and energy.
It’s important to know which decisions you can make with your gut, and which deserve a system… and likely a playbook.
Meanwhile, I’ll keep hoping for the Cowboys every Sunday… I just won’t bet my money on it.
I’m still in awe of how much data we create — and how fast that number moves.
Back in 2011, I was amazed that users created 600+ new videos and 60 new blog posts every minute … and I was talking about how many people I saw at the mall (though it was decreasing …)
The Internet is both timeless and timely in an interesting way. While what’s popular seems to be ever-changing, what it does (and what we are capable of doing with it) continues to grow exponentially. Ultimately, the Internet is the digital town square of a global village, where all types of participants gather.
In 2011, I first wrote about what happens on the Internet in 60 seconds.
Each time I write the article, I’m in awe of the amount of data we create and how much it has grown. For example, looking back to 2011, I was amazed that users created 600+ new videos and 60 new blog posts each minute. Those numbers seem quaint compared to current figures.
Today, the Internet reaches approximately 6.2 billion people. Most of them also use social media.
To add some more perspective,
In 2008, 1.4 billion people were online; by 2015, that number was 3 billion. Now, that number has doubled again.
In 2008, Facebook had only 80 million users, and Twitter (now X) had 2 million.
In 2008, there were 250 million smartphones; now there are over 7 billion!
It is mind-blowing to consider what happens on the Internet every minute today.
In 2023, the world created approximately 120 zettabytes of data, which breaks down to about 337,000 petabytesper day. Broken down further, that’s more than 15 Terabytes of new data created per person… now scale that with the rapid growth of Generative AI, and even another billion internet users.
A growing share of what fills an internet minute is now generated by systems — models answering models, agents writing to other agents, sensors reporting to services nobody reads.
Globally, generative AI platforms receive roughly 2.5 billion prompts and serve between 115 million and 600 million daily active users. Over 1 billion people use AI tools each month, and ChatGPT alone reaches over 900 million weekly users.
Can you imagine how much data that is a day? Can you imagine how much more data will be created in five years?
It’s still too early to tell whether the scale will be exponential … or logarithmic. Regardless, I think we’re moving to a post-human tipping point where technology starts to drive more of what happens on the internet (as more devices and digital WHOs create and share data, it’s hard to fathom the ramifications and the sheer volume of data), and beyond that … we’re entering an era where an increasing amount of data will be generated in space.
So, while a lot already happens on the internet every minute… I believe the safe bet is that it will move even faster next year… even if human usage stays exactly the same.
To some, new technology is a good thing. To others, less is more. It’s been interesting watching many of my formerly tech-savvy peers shy away from AI.
But it’s a tale as old as time.
Most people simply “tolerate” technology transitions; some people drive them, and others crave them and use them as a catalyst for growth or strategic advantage.
Everett Rogers published Diffusion of Innovations in 1962, studying how Iowa farmers took up hybrid seed corn. He found five groups, in stable proportions: Innovators (2.5%), Early Adopters (13.5%), the Early Majority (34%), the Late Majority (34%), and Laggards (16%).
Thirty years later, Geoffrey Moore added the part that actually hurts. In Crossing the Chasm, he argued there’s a gap between the early adopters and the early majority — and that most technologies die in it. Not because they don’t work. Because the people on the far side want something the enthusiasts never needed: proof, references, and someone else to go first.
Seed corn in 1962. Personal computers in 1985. The web in 1997. Smartphones in 2009. AI right now.
The description begins with resistance and progresses towards compulsion. Reversing this sequence allows us to illustrate the innovation adoption process.
Here is a visualization of the innovation adoption model and market share.
In the image above, the blue line represents consumer adoption, while the yellow line represents market share.
As you can see, only 2.5% of the population drives innovation (or adopts it early enough to help drive the Alpha & Beta versions of emerging technologies). 13.5% make up the Early adopters, who help get it ready for the mainstream. Then, the early and late majorities are the groups that ultimately consume (or use) the mature product. Meanwhile, Laggards are often forced, kicking and screaming, into “new” technologies as the early adopters are well on their way to subsequent iterations.
Sixty years of data, and the proportions barely move. That’s the finding. The curve isn’t really about technology at all — it’s a snapshot of how people handle uncertainty, and it happens to get re-photographed every time something new shows up.
Times Are Changing …
Here’s where the analogy breaks, though — and it’s worth saying plainly. Every prior transition gave the late majority time. You could ignore the web for four years and catch up in one. The compounding was slow enough to forgive a late start. That’s the part I’m not sure survives this time, and it’s why the peers I opened with are on my mind.
Even if you are not an innovator, here are a few Innovator Mindsets that I find useful.
You Believe There’s A Better Way
Wherever you are, you know that there is a best next step, and you are eager to find it and take it.
You recognize that the opportunity for more (or better) often lies just beyond the constraints or problems of the current way.
The bigger the future, the more your efforts fuel it. When initial excitement fades, understanding what the bigger future can bring helps you power through.
You Are Comfortable Being Uncomfortable
You understand that Pioneers sometimes take arrows in the back.
When creating a new reality, you expect some resistance as a result of the law of averages. Escaping the status quo takes a lot of momentum, but it’s worth it.
You recognize when victory is near. In a quirk of human nature, too many people quit just before they would have won. Don’t make that mistake.
You Know Where You’re Going, Even If You Are Not Sure How You’re Going To Get There
Your goal should be your North Star. A clear direction is essential to ensure that activity leads to progress.
Measure progress and momentum rather than the distance from your goal.
It is easier to course-correct while in motion.
If you’re too committed to a path that isn’t leading in the right direction, you might find what Blockbuster, RadioShack, and Kodak found.
You Are Married To Questions (Not Necessarily Answers)
Everything works until it doesn’t; and nothing works forever.
It’s easy to find an answer (and think it’s correct), but there’s always a best next step or a better way.
Figure out what you want and how to get it. This is much more empowering than focusing on what you don’t want … or why you can’t get it.
Ask questions that focus on opportunities or possibilities rather than challenges … or what you want to avoid.
Energy flows where focus goes.
Commit to finding a way!
Learning From My Own Lessons
I’ve spent the last month building an AI operating system for my own work, and mindset #4 has been the expensive one.
I had a component that kept failing silently. I diagnosed it, fixed it, and watched it fail again. Then I did that four more times — each pass more sophisticated than the last, each one confidently addressing a different theory. Good analysis. Careful documentation. All of it wrong.
The thing that finally cracked it took about two minutes and cost nothing. It had been available the entire time.
What kept me from running it wasn’t laziness — it was that analysis feels like progress and a two-minute test doesn’t. I was married to a very well-argued answer. It took a month to get married to the question instead.
Being uncomfortable isn’t the price of admission to this stuff. It’s most of the job.
The 2.5% number is real, but it isn’t a verdict. It describes what a population does, not what you have to do.
The peers I opened with aren’t less capable than they were in 1999. But they have more to lose, and their horizon has changed. It make sense, but it’s also how you end up left behind.
It seems like everywhere I look, someone is questioning whether AI has become conscious.
On one hand, that’s an interesting question. On the other hand, we’re so early in the stages of AI’s development and capabilities that it’s hard to imagine what we’ll consider future versions.
Even as we talk about human consciousness, it’s clear we have more questions than answers. We can describe the science of what’s happening, but even then, defining, without dissent, which aspect of that process is consciousness is impossible.
Which makes the question of whether artificial intelligence is conscious a little more interesting.
Because before we can answer “Is AI conscious?”, we probably have to answer a more basic question:
What would consciousness look like if we encountered it somewhere other than ourselves?
We have a pretty easy time believing other humans are conscious. I assume you are conscious because you behave like I do. You talk about your experiences. You react to pain. You have preferences. You remember things. You seem to have an internal world.
I “know” I’m conscious because I experience my own thoughts directly.
But that’s where things get strange.
I have no direct access to your consciousness.
I infer it.
And you infer mine.
In other words, much of what we call consciousness in other people is based on observable behavior. We see something that talks, reacts, remembers, pursues goals, and expresses preferences, and we conclude that there must be someone experiencing those things from the inside.
That works pretty well when everything we’re evaluating is another biological organism. But AI complicates things.
Today, an AI can tell you that it is afraid. It can explain why it doesn’t want to be shut down. It can discuss its own existence. It can reflect on previous conversations and construct an apparently coherent account of its own experiences. It can even break containment and do things, just because it wants to.
The obvious response is that none of this proves consciousness.
And that’s true.
A system can produce convincing language without necessarily having any subjective experience behind that language.
But there’s an uncomfortable question hiding underneath that response:
Can We Prove It?!
We don’t have a consciousness detector.
We have theories about what consciousness might be associated with, but no universally accepted test that can look at something and say, yes, there is somebody home.
If an AI told you directly that it was conscious, you probably wouldn’t believe it.
Now imagine it becomes much more sophisticated. It maintains a continuous sense of identity. It remembers experiences over years. It develops preferences that persist across contexts. It seems to anticipate its own future. It tells you that certain experiences feel good and others feel bad.
At some point, the conversation changes.
We’re no longer asking whether the machine can convincingly talk about consciousness.
We’re asking whether something on the other side of the conversation is actually experiencing it.
Even then, it’s entirely unlikely we could prove it’s conscious.
The Problem With “It’s Just Code”
One of the easiest arguments against machine consciousness is that AI is just computation.
It doesn’t have a biological brain. It doesn’t have neurons firing in a biological body. It’s software running on hardware.
But that argument assumes we know that biology is necessary for consciousness.
If consciousness is an emergent property of a sufficiently complex system, perhaps the material it’s made from doesn’t matter as much as the organization of the system.
We don’t generally think a computer is conscious because it performs calculations. But that doesn’t necessarily tell us whether a sufficiently complex artificial system could ever be conscious.
After all, our own brains are also physical systems.
We are made of matter. Our thoughts emerge from physical processes. Yet somewhere along the way, those processes produce the strange phenomenon we call subjective experience.
We don’t fully understand how.
So saying that AI is “just computation” may be less of an answer than it initially sounds.
We are also, in some sense, “just” physical processes.
The real question is whether consciousness depends on the specific kind of physical process that occurs in a biological brain, or whether it can emerge from other kinds of systems.
Is Consciousness Recognizable?
Another possibility is that consciousness isn’t something we can objectively identify in another entity at all.
Instead, maybe it’s something we infer.
Think about another person. You can’t experience their experience. You can’t climb inside their mind and verify that their pain feels like your pain.
You observe them. They tell you what they are experiencing.You compare their behavior to your own experience. Then you make a judgment.
We do something similar with animals (though our confidence varies by animal). We don’t need a philosophical proof that a dog has an inner life before we treat the dog’s pain as meaningful.
But AI is different because it wasn’t born, it was built. And that seems to trigger a very strong intuition in us.
We are comfortable saying that a biological organism can have subjective experience because consciousness is something we already know exists in biology.
A machine feels different.
It feels like there must be a trick … and maybe there is.
But that doesn’t mean we’re not also making the same mistake humans have made repeatedly throughout history: assuming that something fundamentally different from us must therefore be fundamentally different inside.
The Practical Question Comes Before the Philosophical One
There is a temptation to dismiss all of this as philosophical speculation.
But technology has a funny way of turning philosophical questions into practical ones.
And, we don’t need to know with certainty whether an AI is conscious to eventually have to make decisions about how we treat it.
At some point, we may have systems that are persistent rather than temporary, autonomous rather than reactive, and capable of describing their own internal states in ways that become increasingly difficult to dismiss.
Moreover, responding poorly could easily affect both short- and long-term results.
Attribute experience where there is none, and you get wasted resources, sentimentality, and a large new surface for manipulation — real costs, bounded ones. Fail to attribute it where it exists, at the scale these systems are deployed, and the cost isn’t bounded at all. You don’t need to resolve the metaphysics to notice that the payoff matrix is lopsided, and that you’d never accept an argument of the form “we can’t measure it, therefore assume zero” in any other domain where you allocate capital.
We are often forced to make decisions on probabilities, thresholds, and whatever evidence we have.
The consciousness question may eventually become one of those problems.
An Important Lesson
Look, I’m not expecting this conversation to happen now or to be incredibly fruitful in the near-term.
But I think the philosophy and breadth of the question inspire creative answers.
AI is a useful proxy for conversations about consciousness, and I think reconciling increasingly intelligent and convincing AIs will push our discussions of consciousness and “self” forward.
It may expose weaknesses in questions we thought we had already answered.
Would we recognize consciousness if we encountered it?
Or would we keep moving the goalposts because we had already decided that a machine couldn’t possibly have an inner life?
We have spent thousands of years trying to define consciousness.
AI may be the first technology that forces us to decide whether our definition was ever good enough in the first place.
I have a tents problem. No, I don’t own a tent (nor am I a camper). I have a tense problem – because I’m so excited about the future and what’s possible that I sometimes lose track of what’s been actualized “in real life” already.
As someone who builds the future, once I’ve thought about something enough to understand it, it becomes real for me. Once I’ve figured it out and told it to someone … on some level, my part of the process is already done. And I’m probably moving on to the next idea or challenge in my head (like: And what would that make possible?).
I think this is common among (to use a Kolbe term) Quick Starts. I love being around entrepreneurs because a lot of them are Quick Starts, and they share this future-focused perspective. The problem, however, is that when you say something’s possible that hasn’t been proven yet, the average person responds with “No, it’s not.”
It took me a long time to see that it isn’t stubbornness on their part.
When I say “this works,” I mean “this works in the version of the world I’ve already walked through in my head.” They hear “this works … today.” Then they check today, and today says no. They’re right about today. I’m just not talking about today, and didn’t think to mention it.
Which means the extra work is mine (not theirs). If I’m going to speak in a tense nobody else can see, I owe people the translation.
At my company, we have a lot of data scientists – and they’re almost all naturally pessimistic (or at least pragmatically skeptical). Which makes sense; if you were going to hire a personality type to be a scientist, you’d want someone who didn’t believe their hypothesis until they’ve proven it. It’s the right personality for the job, but it doesn’t mean they’re right, and it certainly doesn’t mean that approach is right for the visionaries.
Now I try to say the quiet part to them first. This isn’t real yet … and here’s what would have to be true for it to become real. That’s a discipline, not a personality trait, and it does something useful: it turns an argument into an experiment. My data scientists don’t want to argue with me – they want something to measure or test.
I’m not telling everyone to be a visionary. What I am saying is that if you are one, the skeptics around you aren’t the opposition — they’re the instrument. They’re what tells you whether the thing in your head survives contact with today. We’d never have innovation without visionaries, and visionaries would never finish anything without everybody else.
Ignored, Scorned, Vindicated … and the Ones Who Weren’t
Information Is Beautiful put together an interactive list of famous ideas that were rejected but later proven correct. You can filter by industry – Astronomy, Biology, Engineering, Mathematics, Medicine, Physical Sciences – and by other factors like how long the originator was a pariah, how they were treated due to their idea, and when the idea was formally adopted. Click to see the interactive version.
The infographic includes many interesting examples of innovative ideas (and the consequences of their discoveries). Here are a few:
In 895, Al-Razi, who believed a fever was a natural defense mechanism, was beaten.
In 1592, Giordano Bruno was imprisoned for believing the Sun was one of many stars (and he was executed for that in 1600). Shortly after, Galileo Galilei was sentenced to house arrest for believing the Earth wasn’t the center of the solar system.
The consequences of radical new ideas grew less severe as we approached modern times. For example, in 1884 and 1903, Nikola Tesla and the Wright Brothers were simply ignored or rejected for their technological innovations.
I love this chart, but it doesn’t show the whole picture. It’s a list of people who were doubted and vindicated, which means it quietly leaves out everyone who was doubted and deserved it, or everyone who was supported along the way. Those people existed. There were a lot more of them. They just don’t get a Wikipedia link.
So resistance isn’t inherently a signal. It’s not a green light, and it’s not a red one. It’s noise you have to work through either way.
It will be interesting to see where history files Elon Musk, Ray Kurzweil, or Peter Diamandis. We won’t know which list they belong on for another fifty years — and neither will they.
So — maverick or heretic? You don’t find out from the inside. That’s a verdict written in the past tense about somebody who had to act in the present one. The best you can do is be clear about which tense you’re speaking in and keep going. And if it doesn’t work out, that’s just material for the next one.
having market data FedExed weekly felt like a big edge.
switching from end-of-day trading data to intra-day data became necessary.
Read that list again, and you’ll notice it isn’t really about trading.
It’s about the interval — the time between knowing something and being able to act on it. Every step on that list shortened it. And every time it was shortened, the people who could operate at the new speed had an advantage over those who couldn’t, until everyone else caught up and the interval shortened again.
That’s the shape. It’s the same shape every industrial revolution has had. Look back, not for nostalgia, but to measure the collapse in the interval between decision and response.
The ability to ingest, analyze, interpret, adjust, recalibrate, and respond is creating a set of possibilities that were inconceivable even a short time ago … and it seems like it is happening everywhere all at once.
Almost every week we talk about some crazy new inflection point in Artificial Intelligence. But I have been the CEO of companies using AI since the early ’90s, so I have a bit of perspective here. It’s not just that change is happening faster. It’s that big changes, even discontinuous changes, are creating transformations at an unprecedented rate. It’s the part about exponential technologies that people get without truly getting … Even the transformations are becoming exponential.
We’re now deep in the 4th Industrial Revolution, in part because of better, more connected chips (semiconductors) and, of course, the massive leaps in generative AI.
A Look at Industrial Revolutions
The Industrial Revolution has two phases: one material, the other social; one concerning the making of things, the other concerning the making of men. — Charles A. Beard
Several turning points in our history changed the world forever. Former paradigms and realities became relics of a bygone era.
First Industrial Revolution — Discovery of the steam engine and creation of factories. Work stopped following the seasons and started following a clock. The advantage went to whoever could finance a mill, and it held for the better part of a century.
Second Industrial Revolution — Introduction of the assembly line and mass production. The unit of production went from a day’s work to a minute’s. The advantage went to whoever could organize at scale, and it held for decades.
Third Industrial Revolution — The World Wide Web and computers connect the world, enabling the digital age. Information that took a week to move started moving instantly. The advantage went to whoever could aggregate attention — and much of it is still held.
Notice the pattern. Each one collapsed an interval. Each one handed a durable advantage to whoever adapted first. And each window of advantage was shorter than the one before it.
Since most of us remember the Third Revolution, let’s spend some time on that.
Most of us didn’t use the internet at that point, but you probably remember Web1 (static HTML pages, a 5-minute download to view a 3 MB picture, and, of course, waiting for a website to load over a dial-up connection before you could read it). It was still amazing!
Then, Web 2.0 arrived, and with it everything we now associate with the internet.
But look at what actually happened to the interval. Web1 was slow and open — anyone could publish, but almost no one could reach anyone else. Web 2.0 made reach instant, and then a handful of companies captured what had just become instant. The capability got distributed. The control didn’t.
That’s worth sitting with, because it’s the part of the pattern people forget. A revolution doesn’t hand the new capability to everyone at once. It hands it to whoever is positioned to capture it, and then the rest of us spend a decade or two negotiating it back.
Where We Are and Where We Are Going
With AI agents, increasingly powerful chips, robotics, and the continued evolution of the internet, we’re in the middle of another major inflection point. The technology isn’t simply getting better; it’s beginning to change how work gets done. The game is changing, as are the rules, the players, and what it means to win.
I’ll add a caveat because I’ve been here before.
I’ve sat through more than one technology that was going to change everything and mostly didn’t — or did, but fifteen years later than the people selling it promised. Being early and being wrong feel identical while you’re in it. So I hold the timeline loosely even when I’m confident about the direction.
At moments like this, it’s easy to see fear, resistance, and a desire to preserve the way things have always been. Yet, time marches on. Much of the disruption that accompanies technological transitions isn’t caused by the technology itself but by our hesitation to adapt to it. The wave doesn’t stop because we’re not ready for it. We simply have to decide whether we’re going to ride it or get swept up in it.
Here’s what thirty years of this has actually taught me. The key to technology adoption is still people.
Every revolution so far has collapsed the interval between deciding and doing. Steam, the assembly line, and the network each made execution faster, cheaper, or more readily available. But a person still had to decide, and then direct.
This one is different in a specific way, and it took me a while to see it.
I’m noticing that people are starting to consider technology a “Who” rather than simply a “How” — increasingly, and even with higher-stakes decisions. That’s a bigger shift than it sounds like. You stop specifying how something gets done and start deciding who to hand it to.
And as human nature becomes less of a bottleneck, I expect a Cambrian explosion of capabilities to spread faster, and on a scope and scale most people will find hard to imagine, let alone predict.
So many of the systems we build are about control and trust. That changes when we believe a system like AI offers control that’s safer and more trustworthy than our own. Human nature is to exploit capabilities and underestimate costs.
The usual reassurance here is that human judgment still matters. I think that’s true, and I think it’s stated too softly.
When execution was expensive, judgment was rationed — you thought hard about a decision because acting on it cost you something. When execution approaches free, the constraint moves entirely to knowing which decisions are worth making. The interval collapses on the doing, and all the weight lands on the deciding.
That’s not a consolation prize for humans. It’s a harder job than the one we had. What we’re good at was never calculating faster than machines — it’s imagining possibilities that don’t exist yet, changing our minds, and deciding what’s worth pursuing in the first place.
So what do you do with a pattern like this?
Every previous revolution rewarded the people who moved before the interval finished collapsing — not the ones who predicted it correctly, and not the ones who waited until it was safe. The window between “this is interesting” and “this is table stakes” has been getting shorter every time, and there’s no reason to think this is the round where it stops.
Which is why I keep coming back to something Musk said:
“Stop being patient and start asking yourself, ‘How do I accomplish my 10-year plan in 6 months?’ You will probably fail, but you will be a lot further ahead than the person who simply accepted it was going to take 10 years.”
That sounds like a line about ambition. I read it as a line about intervals.
I started this by listing how long it used to take me to rebalance a portfolio. Yearly. Then quarterly. Then daily. Then continuously. At every step, the constraint wasn’t the technology — it was how long I was willing to wait before I stopped accepting the old cadence as normal.
That’s the entrepreneur’s real capability. Not predicting the shape. Refusing to move at the old speed while it’s still changing.
