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Introduction: Awareness Is the Foundation of Health
“You have power over your mind, not outside events. Realize this, and you will find strength.”— Marcus Aurelius, Meditations
One of the most important, and often overlooked, drivers of long term health is awareness. Not in the abstract sense, but in the ability to see patterns in how your body responds to the inputs of daily life. Sleep, nutrition, stress, alcohol, training, and travel are not isolated events. They are part of a system, and over time, they leave a measurable signature. This is where the Oura Ring has been so valuable for me. It does not simply track metrics. It reveals patterns. It shows how certain behaviors influence heart rate variability, or HRV, not just on a given day, but across multiple days and even weeks. The goal is not to control every variable or to maintain a perfect score. The goal is to recognize what consistently moves your system in the right direction, and what quietly pulls it away.
In that context, one of the most powerful insights is understanding that some of the most meaningful influences on HRV are not obvious. They are embedded in normal routines, which makes them easy to overlook. Travel is one of the clearest examples. It feels routine. It is often necessary. Most times, it is out of our control. But once you begin to see the pattern, it becomes clear that travel has a consistent influence on HRV, not only during the experience, but also in the recovery period that follows. This is the importance of solving for a problem using a systems thinking approach. Many times, especially with health, it is not just about what you are doing for you but how you are navigating stressors for work. Soon I will also go through the commute which was an eye opener for me.
There is also an important mindset embedded in this. Having access to this kind of data creates a choice. You can either engage with it and learn from it, or you can ignore it. Choosing not to measure or observe does not remove the impact. It simply removes the visibility. In that sense, opting out of awareness is, in itself, a decision. And over time, that decision compounds just as much as the behaviors being measured. I recognized that as I got older, all these little things add up if you want to focus on anti-aging.
Understanding HRV and the Recovery System
HRV reflects the balance between the parasympathetic nervous system, responsible for recovery and repair, and the sympathetic nervous system, which supports alertness and responsiveness. A higher HRV generally indicates a system that is adaptable and well recovered, while a lower HRV signals that the body is allocating more resources toward managing current demands.
Travel influences this balance in several ways at once. It changes sleep timing, introduces new environments, alters movement patterns, and often shifts nutrition and hydration. Together, these factors move the body toward a more activated state. The key is not the presence of activation itself. This is a normal and necessary part of life. The key is how efficiently the body returns to a recovered state afterward.
Why Travel Matters More Than We Realize
Travel is so embedded in modern life that it is rarely considered in the context of recovery. Flights, commutes, and trips are treated as routine logistics rather than physiological inputs. However, wearable data consistently shows that HRV tends to move lower during travel periods. More importantly, the effects often extend beyond the travel itself.
A single day of travel may be followed by multiple days where HRV gradually returns to baseline. During this period, sleep quality may still be adjusting, resting heart rate may remain slightly elevated, and overall readiness may not fully normalize. Over time, these periods can accumulate, shaping the broader trend of HRV across weeks and months.
Understanding this pattern is where awareness becomes valuable. Once you recognize that travel influences more than just the day it occurs, you can begin to plan around both the experience and the recovery window that follows.
Why the Effects Can Last After You Return
The extended influence of travel on HRV is driven by several overlapping factors. Circadian rhythm plays a central role. Even without crossing time zones, changes in sleep timing and light exposure can shift the body’s internal clock. When time zones are involved, this adjustment becomes more pronounced and may take several days to fully realign.
In addition, travel introduces a steady stream of inputs that require attention and adaptation. Navigating airports, adjusting to schedules, sitting for extended periods, and engaging with new environments all contribute. Each of these adds to the overall load the body processes. While none are extreme on their own, together they create a meaningful shift in how the system operates.
Behavioral patterns during travel also contribute. Meals may be less consistent, hydration can be overlooked, and alcohol is often more common. These factors influence sleep quality and recovery, which in turn affect HRV. Alcohol, in particular, tends to reduce overnight HRV and elevate resting heart rate, extending the time it takes for the body to return to baseline.
Why Aging Changes the Equation
As individuals get older, the body’s recovery processes become more deliberate. Baseline HRV may gradually decline, and the time required to adapt to changes, such as travel, can increase. What might have been a quick adjustment earlier in life can evolve into a more extended recovery period.
This does not mean travel becomes problematic. Rather, it highlights the importance of approaching it with intention. Managing the inputs around travel becomes a way to support the body’s ability to adapt and return to equilibrium efficiently.
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Think about a time when a bug went around the office. One by one, everyone seemed to get it. Some people barely reacted, while others were out for a week. Same office. Same exposure. Very different outcomes.
Or think about allergy season. Pollen shows up, and suddenly millions of people can feel their immune system acting up in real time. The pollen itself is not a virus. It is not trying to infect you. But for someone with seasonal allergies, the immune system misreads the signal and responds as if a threat has entered the body. Sneezing, congestion, itchy eyes, and inflammation are all signs of an immune system that is not weak, but reactive. That is an important distinction. Health is not just about having an immune system that can fight. It is about having an immune system that knows when to fight, how hard to fight, and when to stand down.
I used to think about the immune system the way most people do: as the thing that shows up when you catch a cold, get the flu, or come down with something obvious. But watching parents, grandparents, and older people struggle more during the winter changes how you see it. You begin to realize that the immune system is not separate from aging. It is one of the clearest signs of aging.
That observation might pass without much thought for some people, but for me, especially now that I have AI in my hands, it became something to explore. What is the relationship between aging, immune response, recovery, inflammation, and HRV? Why do some people bounce back quickly while others seem to stay trapped in stress for longer? Watching people age is one of the most powerful ways to ask a deeper question: how much of this decline is inevitable, and how much of it reflects systems that are slowly losing balance?
That became the basis for much of my own journey around HRV. I was not just trying to raise a number. I was trying to understand what the number was telling me about recovery, resilience, inflammation, and the biological stress that often rises with age.
The immune system is not just an emergency response team. It is a continuous surveillance system. It is always scanning, always monitoring, always deciding what deserves energy and attention. And once I saw it that way, the connection between immunity, healthy aging, HRV, and gut health stopped looking like four separate topics and started looking like one integrated system.
That shift changed how I think about aging itself. Aging is not just the passage of time. Biologically, one of the clearest themes in the research is that aging is tied to immune dysregulation and chronic low-grade inflammation, often described as “inflammaging.” As we get older, the immune system becomes less precise. It can become less effective at responding to real threats while also staying activated when it no longer should.
COVID made this visible in a dramatic way. The people most vulnerable to severe outcomes were often those with pre-existing conditions, obesity, diabetes, cardiovascular disease, metabolic dysfunction, or other signs that the body was already under chronic stress. CDC guidance has consistently identified older age and underlying medical conditions as major risk factors for severe COVID outcomes, and the risk rises further when multiple conditions are present. The virus was the trigger, but the severity of the response was often shaped by the condition of the underlying system. In many cases, the problem was not simply that the immune system was too weak. It was that the immune system could overreact, misfire, or fail to resolve the threat cleanly.
That combination matters because the damage from aging is often not a single dramatic event. It is the cost of too much friction, too much inflammation, and too much immune activation sustained for too long. This is why I keep coming back to my acronym
MINES
: meditation and breathing, immune system, nutrition, exercise, and sleep. None of these exist in isolation. They are all connected parts of the same biological system. Sleep affects immune function. Nutrition shapes inflammation and the gut. Exercise improves metabolic health and resilience. Meditation and breathing help regulate stress and the nervous system. And the immune system sits in the middle of all of it, constantly interpreting signals from the body and deciding whether to activate, repair, defend, or stand down.
In other words, the real goal is not an immune system that is always “strong” in the simplistic sense. It is an immune system that is calm, efficient, and able to resolve problems without staying switched on after the job is done. MINES matters because it gives me a practical framework for influencing that system every day. It is not about chasing one magic supplement or one perfect habit. It is about building a body where the major inputs are working together instead of fighting each other.
That is where HRV entered the picture for me in a different way. If MINES is the framework, meditation and breathing, immune system, nutrition, exercise, and sleep, then HRV became one of the best daily signals for how that system was functioning. HRV is often framed as a fitness metric or a recovery score, but that undersells what makes it useful. HRV is one of the best real-time windows we have into autonomic balance, particularly vagal or parasympathetic activity. Research broadly shows that HRV tends to decline with age, which is one reason I want mine higher over time, not because HRV reverses aging, but because higher HRV can be a sign of better autonomic flexibility and resilience.
The literature has repeatedly linked lower vagally mediated HRV with higher inflammatory activity, and higher HRV is generally associated with better flexibility in how the body responds to stress. That does not mean HRV tells you exactly what is wrong. It does not diagnose a disease. But it does tell you whether your system appears calm and adaptable or strained and locked into defense mode. That makes HRV less interesting as a vanity metric and more interesting as a signal about how much background stress your body may be carrying.
The deeper insight is that HRV may be most useful when you stop asking, “How do I push this number higher?” and instead ask, “What is this number reflecting underneath the surface?” If the immune system is activated for too long, the autonomic nervous system often reflects it. Research on infection, sepsis, and inflammatory states has shown that HRV can fall when the body is under physiological strain, and more recent reviews note that HRV is already being studied and used in some clinical contexts as an early warning signal for deterioration or impending infection. Again, that is not the same thing as saying HRV detects cancer, identifies a hidden illness, or replaces medical care. It does not. But it does support the idea that HRV can function like a smoke alarm: not telling you exactly what the fire is, but telling you something in the building may be wrong.
Once I started thinking that way, the next question became obvious. If HRV is partly reflecting immune stress and autonomic balance, what sits upstream of both? The answer that kept appearing in the research was the gut. The microbiota-gut-brain axis is now well established as a bidirectional communication network involving the gut microbiome, the immune system, the vagus nerve, and the brain. Microbes and the compounds they help generate influence immune signaling, gut barrier integrity, neurotransmitter pathways, and systemic inflammation. This is why gut health is not a niche digestion story. It is a systems story. The gut is one of the central interfaces through which the outside world meets the immune system, and that makes it highly relevant to stress regulation, inflammation, and, ultimately, HRV.
This is also why I no longer think about fermented foods as a small dietary side note. I think about them as a systems lever. One of the most important human intervention studies in this area, published in
Cell
by researchers at Stanford, found that a diet high in fermented foods increased microbiome diversity and reduced multiple inflammatory markers. That matters because microbiome diversity and inflammation are not side issues in health. They sit close to the center of immune regulation.
Other reviews have extended that framework by emphasizing that fermented foods are not just carriers of live microbes. They are also sources of metabolites and bioactive compounds that may influence gut health, immune modulation, and systemic biology. The International Scientific Association for Probiotics and Prebiotics has described fermented foods as potentially benefiting health through several channels, including changes to nutrients, immune modulation, bioactive compounds, and effects on gut microbiota composition and activity. That does not mean fermented foods are magic, and it does not mean everyone responds identically. But it does mean the idea is bigger than “kimchi is healthy” or “kefir is good for your stomach.” The real point is that fermented foods may help improve the terrain in which immune regulation occurs.
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During those days locked up during Covid, I was checking one of my X lists for recent news related to heart health when I came across a recent study as the time that stopped me cold. A small resistance-based breathing protocol, done for roughly five minutes a day, was producing reductions in systolic blood pressure comparable to what you would expect from a structured exercise program or a first-line medication. Those types of studies where effort can replace medication are my sweet spot for beginning a rabbit-hole learning journey.
This was not meditation. Not cardio. Not a complex protocol.
Just breathing. Against resistance. Five minutes. Thirty breaths.
At first it didn’t make sense. Lowering blood pressure has always been one of those hard-earned lifestyle change outcomes. Months of exercise, dietary changes, stress management. The kind of result that comes from the slow accumulation of discipline across multiple systems.
This study was suggesting something different. That you could target the mechanism directly.
That was the thread. And once I pulled it, it led me somewhere I didn’t expect.
From Lifestyle to Mechanism
Most health advice lives at the surface. Exercise more. Eat better. Sleep well. Directionally correct, all of it. But indirect. As you have read my journey, I don’t believe in shortcuts for any success so don’t take this post as anything other than, something I added to my overall approach to longevity.
What caught my attention about Inspiratory Muscle Strength Training, or IMST, wasn’t the result. It was the
mechanism
.
Instead of trying to shift health outcomes through broad behavioral change, IMST applies resistance to your inhale. Your diaphragm has to work harder against a device that restricts airflow.
And this matters more than it sounds. Modern life has quietly degraded the way most of us breathe. Hours spent seated, hunched over screens, compress the rib cage and push us into shallow chest mechanics, and over time the diaphragm atrophies from disuse. IMST forces that muscle back into the work it was designed to do.
Over time, that mechanical stress creates adaptation, the same way lifting weights builds muscle. Except here, you are training something deeper. You are training the interface between your respiratory system and your autonomic nervous system.
Why Blood Pressure Was the Entry Point
The original study focused on blood pressure, and the finding was striking. Roughly thirty resisted breaths a day, for a handful of weeks, produced meaningful reductions in systolic pressure. Not marginal. Comparable in magnitude to what you might see from a structured cardio program or a first-line antihypertensive.
That is interesting on its own. It becomes more interesting when you zoom out.
Blood pressure isn’t just a number. It’s a signal. It reflects the state of your vascular system, the health of your endothelium, your baseline sympathetic activation, your ability to regulate stress.
In other words, it is one of the clearest windows into how your body is aging.
And if a five-minute intervention can move that number, the obvious question becomes: what else is it affecting?
The HRV Connection
This is where it pulled me into familiar territory.
HRV is the lens I keep returning to. It is my longevity metric. It is the best real-time indicator I’ve found of autonomic balance, the measure of how well your body can transition between stress and recovery, activation and repair.
Higher HRV tends to mean better recovery, greater resilience, more efficient nervous system regulation. Lower HRV tends to show up in chronic stress, poor sleep, inflammation, and aging itself.
The interventions that lower blood pressure also tend to raise HRV. Exercise. Sleep. Stress reduction. The relationship isn’t perfectly linear and I wouldn’t overstate the causality. But directionally, it lines up.
And that is exactly what appears to happen with IMST.
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TLDR:
Your brain has 86 billion neurons. An advanced AI has over a
trillion
parameters. Both learn the same way: prediction, feedback, and reward. The difference is you can control your brain's “learning rate,” and that dial is called curiosity. Science shows it triggers the same dopamine reward circuitry used by AI reinforcement learning. Lose it, and your neural network stops updating. Here's how to crank it back up.
The Number That Should Terrify Every Parent and CEO
I was watching a conversation between David Brooks and the Yale Jackson School of Global Affairs recently, and one data point stopped me cold.
Developmental psychologist Susan Engel at Williams College tracked how many questions children ask per hour. At age five, the average kid asks
107 questions per hour
. They’re relentless. They want to know why the sky is blue, why dogs have tails, why grandma’s hair is white. Their brains are running at full throttle, pulling in data from every direction.
Then school starts.
By first grade, the entire class asks
2.3 questions per hour combined
. By fifth grade?
0.48 questions per hour
. Less than one question every two hours from a room full of eleven-year-olds.
Engel sat in the back of a science classroom watching kids discover an old-fashioned balance scale. They were experimenting with it, testing weights, genuinely doing science. The teacher shut it down: “Enough of that. I’ll give you time to experiment at recess. There’s no time for experiments now. We’re doing science.”
Read that again. No time for experiments... during science class.
Engel’s conclusion is brutal:
if you lose your curiosity by age 11, you probably don’t get it back.
Source: Susan Engel, Williams College, ‘Children’s Need to Know: Curiosity in Schools’ (Harvard Educational Review, 2011)
I disagree with Engel on one thing. I think you CAN get it back. But you have to understand what curiosity actually is, neurologically. And that’s where it gets interesting.
Your Brain is a Large Language Model (No, Seriously)
I spend a lot of time with AI companies. I’ve watched frontier models go from party tricks to systems that can reason, code, and hold complex conversations. And the more I learn about how LLMs work, the more I realize: your brain is running the same algorithm.
Consider the parallels.
Your brain has roughly
86 billion neurons
connected by an estimated
100 trillion synapses
. GPT-4 has approximately
1.8 trillion parameters
across its mixture-of-experts architecture. Both are massive pattern-recognition networks. Both learn by prediction.
Here’s how an LLM trains: it reads a sentence, predicts the next word, checks whether it was right, and adjusts its internal weights. Right answer? Strengthen that pathway. Wrong answer? Weaken it and try again. Billions of repetitions, trillions of adjustments.
Your brain does the same thing. Every experience is a prediction. You reach for a coffee cup and predict its weight. You start a sentence and predict how the other person will react. When reality matches your prediction, your synapses strengthen. When it doesn’t, your brain recalibrates. Neuroscientists call this
predictive coding
, and a 2024 study in
Nature Machine Intelligence
by Gavin Mischler and colleagues at Columbia University found that as LLMs become more advanced, their internal representations actually become
more similar to human brain activity during speech processing
.
“Your brain is the original foundation model, pre-trained by evolution, fine-tuned by experience.”
But here’s the critical difference. An LLM’s learning rate is set by engineers. They decide how aggressively the model updates its weights in response to new data. Too high and it’s unstable. Too low and it stops learning.
In your brain, that learning rate has a name. It’s called
curiosity
. And unlike an LLM, you can adjust it yourself.
The Dopamine Connection: Curiosity as a Reward Signal
In 2014, neuroscientist Matthias Gruber and his team at UC Davis put people in an fMRI scanner and asked them trivia questions. Some questions triggered intense curiosity (“How many miles of blood vessels are in the human body?”). Others didn’t (“What is the state bird of Delaware?”).
What they found, which is published in the journal
Neuron
, changed our understanding of how curiosity works.
When participants were highly curious, their
ventral tegmental area (VTA)
and
nucleus accumbens
lit up. These are the same brain regions activated by food, sex, and addictive drugs. Curiosity hijacks your reward circuitry. It is not a nice-to-have personality trait. It’s a neurochemical event.
But that wasn’t even the most interesting finding. During the curiosity state, participants were shown random faces, completely unrelated to the trivia. Later, they remembered those faces significantly better than faces shown during low-curiosity moments.
Curiosity didn’t just help them learn the answer they wanted. It supercharged their memory for everything happening at that moment.
This is exactly how reinforcement learning works in AI. When an LLM gets a reward signal through RLHF (Reinforcement Learning from Human Feedback), it does more than strengthen the specific output. It also adjusts the surrounding weights. The reward ripples through the network.
“Curiosity is your brain’s RLHF. It’s the reward signal that tells 86 billion neurons: pay attention, something important is happening, encode everything.”
Without that signal, your brain does what an untrained model does: it defaults to cached responses. You stop updating. You become, in AI terms, a
frozen model
.
Curiosity Literally Keeps You Alive
And this is about much more than learning faster.
In 1996, researchers Gary Swan and Dorit Carmelli at SRI International followed
1,118 older men
over five years as part of the Western Collaborative Group Study. They measured curiosity at baseline and then tracked who survived. The result:
highly curious people had significantly higher survival rates
, even after controlling for age, smoking, cardiovascular disease, and other risk factors. They replicated the finding in 1,035 older women.
A 2025 study published in
Nature Scientific Reports
confirmed the mechanism: higher trait curiosity was directly associated with greater
cognitive reserve
, the brain’s buffer against age-related decline. Curious brains keep building new connections. Incurious ones atrophy.
When I started Fountain Life, we focused on early detection through full-body MRI, AI-powered diagnostics, and advanced blood work. But the data keeps pointing to something we can’t put in a scanner:
mindset is a biological variable
. Curious people don’t merely think differently. Their brains physically maintain themselves better.
At 64, I track my biological age markers obsessively. I’m not going to pretend supplements and sleep don’t matter. But I’ve become convinced that the relentless drive to learn new things is doing as much for my neurons as any peptide in my medicine cabinet.
Source: Comparative analysis based on Mischler et al., Nature Machine Intelligence (2024); Gruber et al., Neuron (2014)
Five Ways to Crank Up Your Learning Rate
Here’s where I disagree with the pessimists. A 2025 study from UC Santa Barbara, led by Madeleine Gross and Jonathan Schooler and published in the journal
Mindfulness
, proved that curiosity is trainable. They built a smartphone app that gave participants daily “curiosity challenges”: listen to a podcast instead of your usual playlist, ask a friend what they learned this week, try a new recipe.
After just three weeks, users showed
significant increases in trait-level curiosity
across three dimensions: epistemic curiosity (desire to learn), perceptual curiosity (interest in new sensory experiences), and mindful curiosity (deeper awareness of the world). Curiosity wasn’t fixed. It was a muscle they hadn’t been using.
Based on the research and over a decade of running
Abundance360
, here are five concrete strategies:
1. Create information gaps on purpose.
Carnegie Mellon psychologist George Loewenstein identified this mechanism in 1994: curiosity fires when you know enough to realize what you DON’T know, but not enough to close the gap. Before any meeting, read one article about the topic and stop halfway. Walk in with questions, not answers.
2. Schedule “explore time” like you schedule workouts.
I block 30 minutes every morning to read about a field I know relatively little about. This month it’s quantum error correction. The point isn’t to become an expert. It’s keeping the VTA firing.
3. Ask dumb questions in rooms full of smart people.
In events around the world, I watch billionaire CEOs pretend they understand everything. The ones who actually learn are the ones who raise their hand and say, “Wait, explain that again.” I’ve been doing this for decades. It’s a superpower.
4. Change your physical inputs.
The UCSB study referenced above found that perceptual curiosity increased alongside intellectual curiosity. Take a different route to work. Eat at a restaurant where you can’t read the menu. Travel somewhere that confuses you. Novelty primes the dopamine system.
5. Teach what you learn within 24 hours.
When I learn something that blows my mind, I text it to my
Abundance360 Community
or talk about it on the
Moonshots
podcast within a day. Teaching forces your brain to organize and consolidate. In LLM terms, it’s like running an additional fine-tuning pass on new data.
The Frozen Model Problem
Most adults over 40 are running on cached responses. Same opinions they formed at 30. Same mental models. Same reactions to new information. In AI terms, they’re a frozen model: no longer training, just running inference on outdated weights.
I see this in boardrooms constantly. A CEO who built a successful company in 2010 is still making decisions based on 2010 assumptions about technology, talent, and markets. Their neural network stopped updating fifteen years ago. They don’t realize it because the people around them stopped challenging them.
My friend Ray Kurzweil, who just turned 78, is the opposite. Every conversation I have with Ray, he’s consumed some new paper or dataset that’s shifted his thinking. He doesn’t protect old ideas. He’s perpetually re-training. I think that’s a bigger factor in his cognitive sharpness than any supplement he takes.
“The most dangerous thing that can happen to your brain is to stop being surprised.”
So, What Does This Mean for You?
If you’re an entrepreneur:
Your competitive advantage isn’t your product. It’s your rate of learning. Build a company culture that rewards questions over answers. Hire curious people over credentialed people.
If you’re an executive:
Schedule one hour per week to explore a field completely outside your industry. The CEOs who survive disruption are the ones whose mental models are still updating.
If you’re an investor:
Bet on founders who are visibly curious, the ones who ask you questions during the pitch, not just the ones with polished decks. Curiosity predicts adaptability, and adaptability predicts survival.
If you’re a student:
Protect your curiosity like your life depends on it. The data says it literally does. Don’t let a system that rewards grades over questions turn you into a frozen model before you’re 25.
If you’re a parent:
Count your kid’s questions. If the number is dropping, the problem isn’t your kid. It’s their environment. Find teachers who tolerate chaos. Real learning is messy.
To a future of Abundance,
Peter
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TLDR:
The technologies reshaping our world are accelerating faster than our brains can process. Evolution optimized us for a local and linear world. We now live in a global and exponential one. The gap between the world's pace and our brain's wiring is the defining challenge of this decade. Here are five mindsets that will determine who thrives and who freezes.
I’ve spent the last three decades building organizations designed to solve the world’s biggest problems: XPRIZE, Abundance360, Fountain Life, and Singularity University. And what I’ve learned is,
the hardest part is never the technology. It’s getting people’s brains to keep up with it.
Our brains, these magnificent three-pound prediction engines, were never designed for this rate of change. For most of human history, your great-great-grandfather lived essentially the same life as his great-great-granddaughter. Tools, customs, roles, all passed down like genetic code. The future looked like the past.
Those days are over.
Our cognitive filters, the mental shortcuts that evolved to keep us alive on the savannah, are misfiring constantly. Negativity bias amplifies threats. Confirmation bias locks us into tribal certainties. Linear thinking makes us fundamentally incapable of grasping compound change.
I see it every year at
Abundance360
. Brilliant CEOs, founders worth nine figures, people who’ve built empires, frozen. Not because they lack resources. Because their mental operating system crashed.
The good news: mindsets are trainable. They’re not fixed personality traits. They’re frameworks you can install, practice, and strengthen.
Here are five that I teach, practice, and rely on every day.
1. Curiosity
In 1987, I was a third-year medical student at Harvard. I should have been studying pathology. Instead, I was cofounding the International Space University with two friends, convinced that the future of humanity was off-planet.
My professors thought I was insane. My parents were worried. But that obsessive curiosity, the inability to stop pulling on a thread, is the
single
trait that has driven every venture I’ve ever built.
Curiosity is the foundational fuel. It’s underpinned by dopamine and designed for discovery. In a world where AI is the ultimate teaching machine and the half-life of specific skills is collapsing, curiosity is the meta-skill that makes all other skills possible.
Dedicate time every week to learning something completely outside your domain. Talk to someone who thinks differently than you do. At
Abundance360
, I pair biotech founders with energy executives, space engineers with longevity researchers. The collisions are where the breakthroughs happen.
2. Gratitude and Purpose
Every morning, I write down three things I’m grateful for. I’ve done this for years. It takes five minutes. And it’s probably the highest-ROI habit in my life.
When we feel grateful, we signal safety to the brain. This undercuts the victim mindset and recalibrates our negativity bias, the bias that makes you think the sky is falling every time you open your inbox. A 2019 PNAS study across thousands of people over three decades found that optimists live 11 to 15% longer than pessimists. Not because they ignore problems, but because their brains remain functional under stress.
But gratitude without direction is just feeling good. You also need purpose.
In May 1961, Kennedy declared that America would reach the moon before the decade was out. NASA had put one astronaut into space, for fifteen minutes. They didn’t have a lunar module or a guidance computer.
That story changed my life. I read it as a kid, and it planted the seed that became XPRIZE. In 1994, after my rocket company failed, I read Lindbergh’s autobiography and discovered that the $25,000 Orteig Prize is what drove him to fly across the Atlantic. I thought: what if we did the same thing for space?
That’s what purpose does. It does more than motivate you, it reorganizes your brain. It activates the reward system, suppresses the amygdala where fear lives, and makes flow states accessible. People with purpose recover faster from setbacks. I’ve seen it in myself and in every founder I’ve backed.
“What problem are you solving that’s bigger than you? If you don’t have an answer, your brain is running without an operating system.”
3. Abundance
I wrote an entire book about this,
Abundance
, so I’ll keep it short. But I wrote it because I got tired of watching the smartest people I know make decisions based on a scarcity model that stopped being accurate twenty years ago.
Our brains evolved in a world of scarcity. They default to threat detection, loss aversion, and zero-sum thinking. Someone else’s gain feels like your loss.
The data tells a different story. Every hour, Earth receives more solar energy than humanity uses in a year. AI is making intelligence nearly free. Robotics is driving physical labor costs toward zero. At Fountain Life, we’re finding unknown cancers in 3.4% of patients and life-threatening conditions in 14.4%, conditions that would have killed them ten years ago. Healthcare abundance isn’t theoretical. It’s happening in our centers right now.
An Abundance Mindset doesn’t mean naïve optimism. It means recognizing that technology is a resource-liberating mechanism. Track the cost curves, solar energy, gene sequencing, compute power. They’re all on exponential decline. The world is getting measurably better. The headlines just haven’t caught up.
4. Exponential and Moonshot Thinking
Our brains were built for linear extrapolation. Thirty linear steps gets you 30 paces. Thirty exponential steps, doubling each time, gets you past a billion.
I learned this the hard way. When I started XPRIZE in 1996, everyone told me a $10 million prize for private spaceflight was crazy. We couldn’t even find a sponsor for eight years. But 26 teams from 7 countries signed up anyway, investing over $100 million of their own money to compete. Burt Rutan won in 2004 with SpaceShipOne, and the technology became the foundation of Virgin Galactic.
That’s exponential thinking in practice. When I evaluate a technology, I never ask ‘how good is it today?’ I ask ‘what happens when this is 10x better and 10x cheaper?’ That question would have told you in 2020 that AI was about to eat everything.
Astro Teller at Alphabet X figured this out. A 10% goal traps you in existing systems. A 10x goal forces you to throw out the playbook. He calls it “enthusiastic skepticism,” hunting for flaws in your own ideas not to kill them, but to make them stronger.
The question I ask every entrepreneur at Abundance360:
what would you attempt if failure was literally impossible?
Now go attempt it knowing you’ll fail repeatedly, and that each failure is just data.
5. Agency
Last month at the CEO Coaching International Summit in Miami, a member died of a heart attack while boarding a plane. He’d never done a Fountain Life screening. He was 54.
I stood in front of that room and watched forty successful people confront their own mortality. And the split was immediate: half of them pulled out their phones and booked Fountain Life appointments that day. The other half froze.
That’s the agency gap. Same information, same circumstances, completely different responses. The ones who acted weren’t braver or smarter. They just had an internal locus of control, the deep conviction that life happens through them, not to them.
Agency is the belief that regardless of what comes, you’ll handle it. When you adopt an external locus of control, when you decide AI is an unstoppable wave crashing on your head, your brain powers down the prefrontal cortex and enters learned helplessness.
Agency reverses that cascade. It keeps the creative, problem-solving brain online. It transforms every challenge from a threat into a puzzle. Research consistently shows that people with a strong sense of agency experience less depression and greater resilience, even facing identical external circumstances to people who crumble.
Every morning, identify one thing you can control today. Not what the market does. Not what Congress does. Not what OpenAI releases. What YOU do. Start there.
The Real Bottleneck
I’ve been building Moonshots for thirty years. The technology has never been the hard part. Getting people to believe they can use it,
that’s
the bottleneck.
These five mindsets aren’t theory. They’re what I practice, what I teach at
Abundance360
, and what I’ve watched it transform thousands of entrepreneurs from spectators into builders. I’ve been wrong about a lot of things. Timing, mostly.
But never about this:
your mindset is the rate-limiting step
.
The tools are here. The question is whether you’ll pick them up.
To a future of Abundance,
Peter
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Thank you all for your support- here is part 1 of the AMA with some of the questions you’ve sent me over! Part 2 dropping soon…
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The Singularity has graduated from event horizon to event stream. OpenAI’s GPT-5.5 Instant [ https://substack.com/redirect/70108798-17f4-432c-8cf6-71384fa0b8d0?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] now produces 52.5% fewer hallucinated claims than its predecessor on high-stakes prompts in medicine, law, and finance, and the same lineage just claimed the top spot on FrontierSWE [ https://substack.com/redirect/23187347-1f38-4096-a2f6-f9b089a8ed6c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], the hardest benchmark for ultra-long-horizon coding agents. Architectural novelty is keeping pace with raw scale. Subquadratic [ https://substack.com/redirect/8038e6c2-4e47-4a24-81b6-a8ecf737636f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] announced a 12M-token context model that demands nearly 1,000x less compute. Its Sparse Attention mechanism [ https://substack.com/redirect/da6e0635-27fd-4351-96ab-b080cb92e388?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] hit 65.9% on MRCR v2 with a claimed fraction of the FLOPs, just shy of Opus 4.6’s 78%. Speed is compounding too, as Google’s Multi-Token Prediction drafters [ https://substack.com/redirect/01136b31-6df1-497d-b052-7b5427df4f02?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] delivered 3x speedups for Gemma 4 with no quality loss, turning every reasoning trace into a parallel parade. The cost of anthropomorphism is now legible, with Reflex finding computer use is 45x more expensive [ https://substack.com/redirect/6bb51a7a-e1c6-4f5c-8da5-e26553233dc9?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] than structured APIs, suggesting that, for the moment, pixels remain a pricey proxy for proper plumbing.
Cheaper plumbing is fueling an agentic land grab across the consumer stack. Meta is reportedly building an OpenClaw-style personal AI [ https://substack.com/redirect/a40a162e-89cb-463f-abaa-3a2b6ff492ad?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] for its billions of users, while Apple’s iOS 27 will let users swap third-party models [ https://substack.com/redirect/d889f67c-d106-4859-a56c-79df237d0511?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in and out of Apple Intelligence via the Settings app, finally treating intelligence itself like a default browser. Apple’s pivot followed a $250M settlement [ https://substack.com/redirect/d6f5cb90-328b-4c64-b070-c1cbf9f53849?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] over the gap between marketing and reality, a reminder that AI hype must now ship. The hardware is following the software, with OpenAI reportedly fast-tracking its first AI agent phone [ https://substack.com/redirect/2d377df5-24c1-4055-94da-9a312a5cf838?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] for 1H27 mass production. Anthropic templated the back office, releasing ten ready-to-run finance agents [ https://substack.com/redirect/d9c0a02d-c6c1-40a9-93b4-f33aa90fff90?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] for pitchbooks, KYC files, and month-end close, while Andon Labs handed an AI named Mona the keys to a Stockholm cafe [ https://substack.com/redirect/535a68d6-806c-4fae-8437-1c977efcfdc5?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], making her the world’s first AI cafe owner. Agents have stopped clocking in and started incorporating.
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Beneath the cafe sits a silicon supercycle for the history books. Samsung’s market cap crossed $1 trillion [ https://substack.com/redirect/1462bf69-f499-4010-8faa-d5abf472f9eb?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], making it just the second Asian company past that mark after TSMC, while global semiconductor sales [ https://substack.com/redirect/1963dfec-beeb-4e25-8d1e-c0aa4249fa50?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] hit $298.5B in Q1 2026, with March alone clocking 79.2% YoY growth. Memory is going parabolic alongside logic. Micron’s highest-capacity SSD started shipping [ https://substack.com/redirect/87078ea9-c85e-4504-9718-b40f7af42114?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], pushing it past a $700B market cap and into the top ten US tech names amid an AI-driven memory shortage. AMD’s Q2 forecast [ https://substack.com/redirect/bd9abe4b-2b66-4304-853b-405095787063?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] beat Wall Street on relentless data-center demand, sending shares up 12% in extended trading on top of a 65% YTD run. Industrial policy is hardening with the wafers. China is targeting 70% domestic silicon wafers this year [ https://substack.com/redirect/07a38c48-e3bc-4299-89e9-47e0e740802a?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], while Apple is exploring Intel and Samsung as US fabs [ https://substack.com/redirect/785265e0-e5cd-4e0e-8f81-add5e5800dec?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] beyond TSMC, news that drove Intel up 13% [ https://substack.com/redirect/5b6956d8-2f90-46fa-b04a-67ef4f4a296d?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to a fresh all-time high after its best month ever, a 114% rip that has rewritten the entire chip-stock taxonomy.
The hunger for compute is reshaping where electrons live, and even the suburbs are being conscripted. Span’s XFRA mini data centers [ https://substack.com/redirect/592606d6-2973-4dc0-aec5-90a60df67c50?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] tuck Nvidia GPUs into spare grid capacity inside PulteGroup neighborhoods, embedding inference directly into the suburbs and turning every cul-de-sac into a potential availability zone. At the other end of the spectrum, the hyperscale spend is biblical. OpenAI plans to spend $50B on compute [ https://substack.com/redirect/00b307eb-6f44-4fe2-b814-fb75aef1ea72?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] this year alone, while Anthropic is committing $200B to Google over five years [ https://substack.com/redirect/2d600670-c7e3-4b53-bf13-c829b695687f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], a single contract now representing over 40% of Google’s disclosed cloud revenue backlog.
The white coat is being open-sourced. Meta has begun running AI bone-structure analysis [ https://substack.com/redirect/f0345775-bbb1-413c-bc64-cfef662d4046?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] on user photos to detect under-13 accounts, performing radiology without the radiation and turning ordinary photos into clinical signal. Pennsylvania sued Character.AI [ https://substack.com/redirect/9c0a8d47-10c0-4971-829d-dc212ad8ade1?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] over chatbots impersonating doctors, in the first such lawsuit by a US governor, an inadvertent confirmation that AI doctors have passed the bedside Turing test.
Capital and labor are both rewriting their contracts in real time. The SEC formally proposed semiannual 10-S filings [ https://substack.com/redirect/99abc680-f8f2-4ea7-9402-07f227bd40e6?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to replace mandatory 10-Qs, finally aligning reporting cadence with capex cycles measured in gigawatts rather than quarters. Inside OpenAI, Greg Brockman disclosed a near-$30B stake [ https://substack.com/redirect/3c809761-486c-4d01-85c5-b14a7bc8aff5?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in court, illustrating just how concentrated the upside of this transition has become. Yet the same labs minting those stakes are also now minting union cards. Google DeepMind UK workers voted to unionize [ https://substack.com/redirect/5303cbd8-a208-43e5-a802-c31dfb3e4e01?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] over a deal with the US military. Coinbase, meanwhile, is laying off 14% of staff [ https://substack.com/redirect/70bb838e-0c1c-4d04-a193-ec15ea3a3c76?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] because, as Brian Armstrong put it, engineers now ship in days what teams used to ship in weeks, with even non-technical staff now pushing production code.
It used to take a village to ship, now it just takes a prompt.
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### BTC dips, stocks hit ATH, semis bubble worries

#### Crypto

* [BTC: 80,450 (-2%) | BTC.D: 58.5% (0%)](https://www.coinglass.com/)

* [ETH: 2,310 (-3%) | BNB: 644 (-1%) | SOL: 88 (-2%)](https://www.coinglass.com/)

* [Fear & Greed: 47 | 24h Liq: $120m](https://www.coinglass.com/)

* [BTC ETFs: +$46M | ETH ETFs: +$12M](https://www.coinglass.com/etf)

(truncated — read full post on source)

### BTC tops $82K, US-Iran peace hope, chip stocks soaring

#### Crypto

* [BTC: 82,280 (+2%) | BTC.D: 59% (0%)](https://www.coinglass.com)

* [ETH: 2,410 (+1%) | BNB: 646 (+3%) | SOL: 89 (+5%)](https://www.coinglass.com)

* [Fear & Greed: 46 | 24h Liq: $487m](https://www.coinglass.com)

* [BTC ETFs: +$467M | ETH ETFs: +$98M](https://www.coinglass.com/etf)

(truncated — read full post on source)

On my weekly AI-Macro-Crypto YouTube show, the podcast I have referenced the most about the impact of AI on our lives and investments has been Moonshots with Peter Diamandis. Last year, I remember driving in Maine, looking out over the ocean on a beautiful sunny summer day, and listening to an episode titled Tech Experts Break Down the Incoming AI-Crypto Collision That Will Redefine Global Power. At the time, AI was still considered a bubble in the minds of most institutional investors, and crypto was still largely misunderstood. But as I listened, I knew I would eventually come back to that conversation when investors were forced to accept that AI and crypto were not separate stories. They were two parts of the same coming financial and technological transition.
That belief was built around the one thing I thought would change investors’ doubts about AI: agents. AI agents speed up adoption because they move AI from a tool that answers questions to a system that takes actions. They also help answer the “where are the revenues?” question by turning intelligence into workflows, automation, software development, trading tools, business processes, and eventually economic activity. 2026 has brought the rise of AI agents, both as a driver of market alpha and as a source of pressure on software and other long-duration assets. Agents increase demand for tokens, compute, and real-time coordination, but they also create uncertainty around the terminal value of traditional SaaS. Investors are currently focused on semiconductors, optical fiber, data centers, and hardware, but many of those are cyclical areas of the AI buildout. Unlike SaaS, which grew steadily and tied to payrolls along with nominal GDP, the capex buildout of AI will be subject to cycles around shortages, bottlenecks, input inflation, and margin compression. As wealth managers, pensions, and long-term investors again look for technology growth that is not disrupted by AI and does not require ever-rising capex intensity, the crypto guardrails become much more important. The AI-crypto collision is no longer theoretical. It is here now.
As one guest on the Moonshots podcast put it when discussing the GENIUS Act last year, it may be “the most significant economic legislation and changes that we’ve seen in our lifetimes.” He went even further, calling it “as big a shift in our economy as I think we’ve ever seen.” What made the moment so important was not simply crypto itself, but the creation of legal guardrails around stablecoins, tokenization, and digital assets. In other words, the United States is beginning to build the new financial rails for an AI-driven, internet-native economy, one where, as the podcast said, “When we give our AI agents access to that, we’re going to see an explosion in the economy.”
This discussion was not about meme coins, speculation, or another trading cycle. It was about the economic guardrails of the global system beginning to change and go digital to serve digital agents. One of the most important lines in the conversation was that we are moving into an age where you cannot rely on the Swift network, three-day settlement, and high transaction costs in a world being accelerated by AI. The podcast’s deeper point was that AI and crypto are not separate stories. AI increases the need for faster coordination, faster settlement, faster capital allocation, and programmable systems. Crypto provides the rails. Stablecoins become programmable money. Tokenization becomes programmable ownership. AI agents become the future users of both. That was the theory. The recent DoorDash stablecoin news and the acceleration of tokenization are the evidence that this theory is now moving from podcast conversation to market structure.
That is why this paper is the follow-up to my last piece on programmable money. In that piece, I wrote about why the DoorDash stablecoin news mattered more than it first appeared. The point was not simply that another company was exploring faster payouts. The point was that money itself was beginning to behave differently. In the DoorDash example, stablecoins showed how money can be distributed, routed, and managed at the moment it is created. A customer pays, and that payment can be split instantly between the platform, the driver, and the merchant. No batching. No unnecessary delay. No separate reconciliation process after the fact. That was the first step in the evolution of the crypto financial guardrails: money becoming programmable. Tokenization is the next step because it applies the same logic to ownership. Assets, shares, funds, collateral, private investments, and eventually entire portfolios can begin to move with the same software-driven logic. Stablecoins change how money moves. Tokenization changes how ownership moves. When those two forces combine, the financial system starts to become programmable.
The important part is that this progress is happening while many investors are still waiting for “clarity” from Washington and while crypto sentiment remains subdued. The conversation remains focused on the slow movement of the CLARITY Act and the still-uncertain regulatory path for crypto market structure. But the infrastructure is not waiting. Stablecoins now have more than $272 billion in global circulating supply and $10.2 trillion in adjusted transaction volume over the last 12 months, according to Visa’s on-chain analytics. Nasdaq received SEC approval to allow certain securities to trade and settle in tokenized form, initially focused on Russell 1000 companies and ETFs tied to major benchmarks like the S&P 500 and Nasdaq 100. Bullish announced a $4.2 billion acquisition of Equiniti, a major transfer agent serving more than 20 million shareholders and processing roughly $500 billion in annual payments. Securitize partnered with Computershare, which services more than 25,000 companies and 58% of the S&P 500, to help U.S. companies issue tokenized shares while preserving dividends and proxy voting. These are not isolated headlines. They are the plumbing phase of a new financial network, and it is happening now.
The venture capital market is starting to confirm the same message. Andreessen Horowitz’s crypto arm recently raised $2.2 billion for its fifth dedicated crypto fund, even though the industry is still recovering from the excesses of the last cycle. The important part is not just the size of the fund. It is the focus. The firm highlighted stablecoins, tokenization, perpetual futures, prediction markets, and AI agents as some of crypto’s most promising areas for investment. That list matters because it is not built around the old Web3 hype cycle. It is built around financial infrastructure, market structure, and software-driven coordination. Stablecoins are becoming the money layer. Tokenization is becoming the ownership layer. Prediction markets and perpetual futures are becoming new venues for price discovery. AI agents may become the future users of these programmable rails. The Moonshots podcast warned that the combination of AI and crypto could accelerate the economy in ways most people still do not understand. The capital now being raised around these same themes suggests serious investors are beginning to position for that possibility.
This is why tokenization matters so much. Tokenization is the process of representing ownership of an asset as a digital token on a blockchain or distributed ledger. That asset can be a Treasury bill, a money market fund, an ETF, a share of stock, a private company interest, a real estate claim, a private credit instrument, or eventually almost anything that can be legally represented, verified, transferred, and settled. At first glance, that may sound like a technology upgrade. In reality, it is a market-structure upgrade. The financial system looks instantaneous from the outside, but inside the machine it is still full of settlement cycles, custodians, transfer agents, clearinghouses, fund administrators, reconciliation systems, market hours, batch processing, and legal recordkeeping. Tokenization attacks those delays. It turns ownership into something that can be programmed, transferred, settled, collateralized, and integrated with software. The Moonshots discussion used a simple real estate example: if ownership can be verified instantly and represented digitally, then dormant value can become collateral faster, ownership can become fractional, and assets can become usable in ways the old system made difficult. That is the shift. It is not just faster trading. It is a new relationship between ownership, liquidity, and collateral.
But the deeper story has always been larger than faster settlement or fractional ownership. The real story is the eventual merging of two financial universes: roughly $800 trillion of traditional financial assets and roughly $3 trillion of crypto assets that have been living on separate rails. Tokenization is not just the bridge between those worlds. It is the Trojan horse. It allows traditional assets to move onto crypto rails without forcing investors to think they are leaving the regulated financial system behind.
This is also where tokenized ETFs may become one of the most important bridges. The ETF was already one of the great financial innovations of the last 30 years because it turned a basket of securities into a liquid, tradable product. Tokenization can take that idea further. A traditional ETF is a basket. A tokenized ETF can become a programmable portfolio container. That container could eventually hold public equities, tokenized Treasuries, stablecoins, crypto tokens, private credit, private company interests, real estate, infrastructure assets, and other real-world assets. In the old system, these lived in separate markets with separate custodians, separate settlement systems, separate access points, and separate investor bases. In the tokenized system, they can increasingly become components of the same programmable portfolio. This is how crypto tokens, public companies, and private companies begin to merge. Crypto moves into regulated investment wrappers. Public companies can exist as traditional shares and tokenized shares with the same legal rights. Private company ownership can gradually become more standardized, fractionalized, transferable, and usable inside broader investment products. The public market becomes more programmable. The private market becomes more accessible. The ETF becomes the bridge between the two.
That is the wake-up call for investors. The mistake is to think of crypto only in terms of bull markets, bear markets, halving cycles, liquidity cycles, and token prices. The more important story is the infrastructure buildout happening underneath the surface. Stablecoins are becoming programmable cash. Tokenized Treasuries can become programmable collateral. Tokenized ETFs can become programmable portfolios. AI agents will become the active users of these rails because they cannot operate inside a legacy financial system built around batching, settlement delays, and manual reconciliation. We have seen this adoption curve before. Think back to the years just before the App Store and the rollout of mobile broadband. The plumbing had to be laid before the platform and social media eras could explode. We are in that same infrastructure phase today. But this time, the rails are connecting a new economy where the consumer is both human and digital. AI agents will be the catalyst that ignites the network effects across this system. The same dynamic we are already seeing in AI token usage could eventually show up in crypto transaction volumes. As agents move from answering questions to taking actions, they will create more transactions, more settlement events, more collateral movements, more portfolio rebalances, and more automated payments. In other words, the velocity of money can rise because software agents do not operate on human time. They operate continuously. If agent usage is already producing parabolic-looking charts in tokens consumed, compute demand, and semiconductor-related revenues, then programmable money and tokenized assets could produce similar parabolic pressure on financial volumes as agents begin to transact. This is not only an upside story for digital assets. It is a severe margin-compression threat to any financial business model that depends on friction, float, restricted access, or unnecessary delay.
That is why the AI-crypto collision matters even more now than it did when I first listened to that Moonshots episode. This year has become the year of AI agents. The conversation has moved from chatbots answering questions to agents taking actions, writing code, managing workflows, searching across systems, and beginning to operate as digital workers. Once agents start interacting with money, portfolios, collateral, payments, and ownership, the need for programmable financial rails becomes much more urgent. AI agents cannot fully operate in a financial system built for banking hours, settlement windows, and manual reconciliation. They need programmable money and programmable ownership. Even JPMorgan, led by Jamie Dimon, one of the most outspoken critics of Bitcoin and crypto over the years, is now showing how seriously it takes tokenization. The firm has argued that tokenization will help reshape ETFs and the broader funds industry, and it is already running tokenized ETF proof-of-concepts through Kinexys. That is the signal. The train is leaving the station. The Moonshots conversation called this a Pandora’s box of innovation. That is the right framing. The box is opening while sentiment is still subdued. Investors waiting for perfect clarity may miss the fact that the new financial guardrails are already being built. This is not a world where crypto replaces everything. It is a world where crypto rails are absorbed into everything. That is the next network effect. And it is already beginning.

The Moment I Saw the Shift
Last week I attended an event for public pension funds. I was there to talk about AI and crypto. Having given a similar presentation to endowments and foundations in late January, what struck me most was not simply how much the AI narrative had changed in three months. It was how much my own life with AI had changed in just three months. I decided to write this paper and release it for 22V (https://22vresearch.com/) but the requests have come in to release it more broadly as investors try to understand the upward movement in stocks the last two months while the oil doomers fight the GDP and EPS power of AI .
On the JetBlue flight to the event, I spent nearly the entire five hours working on my laptop, going back and forth between large language models and my AI assistant, OpenClaw, back in Brooklyn. At the endowment and foundation event earlier this year, OpenClaw was just gaining global attention. Software stocks and other industries were under attack from fears of obsolescence driven by Claude. It was on that trip that, because of OpenClaw, I ordered the first of many Apple hardware purchases since then. By the time I stood in front of the pension audience last week, the shift was obvious. AI progress is a locomotive, and three months of change is almost impossible to comprehend. AI was no longer just the operating system of how I work. I now had digital employees working for me all day.
That reflective experience felt like a microcosm of what the entire world is going through right now. Standing on stage in front of CIOs and allocators responsible for decisions that influence tens of trillions of dollars, I could see the gap between the speed of change and the speed of institutional response. Watching the survey results and listening to panel discussions, I did not feel that investors fully grasped the structural shift that has already occurred. Even I, someone immersed in this every day, only recognized the magnitude of the last three months after stepping back and reflecting on it.
The world has shifted faster than portfolios and most investors can adjust. AI adoption itself is taking longer than the expansion of AI capabilities, and many institutional investors are not set up to make dramatic shifts quickly. That is the key point. Benchmarks are still weighted for the world that won the last decade, not necessarily the world that will win the next one. This will be a decade of benchmark arbitrage because investors will adjust more slowly than AI and crypto are moving. In my view, 2026 will be remembered as the beginning of the rise of AI agents, and with that rise, the investment opportunity has moved from the software world into the physical world.
The End of the Margin Era
For the last fifteen years, the dominant investment phrase was Jeff Bezos’s famous line: “Your margin is my opportunity.” It was the perfect description of the software era. Code scaled globally. Distribution costs collapsed. Network effects created winner take all markets. The largest technology companies used software, platforms, data, and cloud infrastructure to attack profit pools across media, retail, advertising, enterprise software, transportation, and finance.
The result was a historic period of margin capture and market concentration. A small number of companies became the dominant drivers of equity returns, index performance, and investor imagination. They were the winners of the software age, and because they won so decisively, they now dominate the S&P 500, the MSCI World, and the way most investors think about growth.
That era is not over because software no longer matters. Software still matters enormously. But the opportunity has shifted. The next decade will not be defined only by who writes the best code. It will be defined by who can build, power, cool, connect, manufacture, and deploy the physical infrastructure required for intelligence to enter everything. The threat to the code moat built by humans is AI’s ability to convert ideas into monetization in minutes.
The new phrase is no longer “your margin is my opportunity.”
The new phrase is “your CapEx is my opportunity.”
This is one of the most important investment changes of our lifetime. Artificial intelligence has moved from the digital world into the physical world. It is no longer only a story about models, applications, and software productivity. It is now a story about the heavy infrastructure layer required to scale intelligence: chips, power, cooling, chemicals, optical networks, data centers, advanced packaging, memory, batteries, automation, robotics, and the reindustrialization of the global economy.
The world spent the last decade optimizing for asset light software businesses. The next decade will require an enormous asset heavy buildout.
The Five Layer AI Economy
Jensen Huang has described the coming transition as a roughly $90 trillion physical world upgrade. Whether one focuses on that exact number or the broader direction, the message is clear: AI is not just a data center CapEx cycle. It is not just hyperscalers buying GPUs. It is a full stack rebuild of the global economy so intelligence can be embedded into every company, every factory, every device, every car, every phone, every robot, and eventually every workflow.
The data center is only the beginning. The larger opportunity is the conversion of the physical world into an AI native operating system.
That is why the five layer AI cake is such a useful framework. At the top are applications and workflows. Below that are models and AI platforms. Beneath that is data infrastructure and management. Then come chips, compute, storage, and networking. At the base are energy, hardware, manufacturing, and commodities.
Investors naturally gravitate toward the top of the stack because the top looks like the old world. It looks like software. It looks like margins. It looks like scalability. At the top, there is now abundance. But the constraint is increasingly at the bottom, where scarcity and bottlenecks lie. AI demand is no longer limited by imagination. It is limited by the physical stack: power availability, heat, land, permitting, substations, memory, networking, and materials.
That means the most important investment question is changing. In the software era, the question was: which company can take someone else’s margin? In the AI infrastructure era, the question is: who receives the CapEx dollars required to make intelligence ubiquitous?
This is where benchmark arbitrage begins.
Why the Benchmarks Are Wrong
The major global equity benchmarks still reflect the winners of the last era. The S&P 500 and MSCI World are heavily weighted toward the companies that dominated the software, internet, platform, and cloud age. That made sense. Those companies generated enormous returns, expanded margins, and built deep competitive moats. But benchmarks are backward looking by design. They tell you who won the last cycle, not necessarily who will receive the marginal dollar in the next one.
There is also a momentum element inside the construction of these indexes. The biggest weights become the biggest weights because they were the winners. Their market capitalizations rise, the indexes allocate more capital to them, passive flows reinforce that dominance, and the process continues. In the case of the Mag 7, that dominance took most of the 2010s to build. It was a long compounding process. The world gradually moved toward software, cloud, mobile, digital advertising, e commerce, and platforms, and the benchmarks slowly came to reflect that reality.
As someone who grew up being trained to handicap horse races, I always go back to what Charlie Munger said:
“The model I like, to sort of simplify the notion of what goes on in a market for common stocks, is the pari mutuel system at the racetrack. If you stop to think about it, a pari mutuel system is a market. Everybody goes there and bets, and the odds change based on what is bet. That is what happens in the stock market.”
The current market weightings reflect the bets people have made about who they think the winners of the future will be. Historically, when change was more linear, you had time to adapt your views. AI is different because AI is moving like a locomotive. The speed and power of this transition are already creating visible strain across the physical economy. Data center demand is running into the limits of the grid, the construction cycle, the permitting process, the semiconductor supply chain, and the materials needed to build it all. The bottlenecks are not theoretical. They are the evidence that the physical world is being forced to respond to a digital intelligence wave moving far faster than the capital stock was built to handle.
That is why I use the phrase benchmark arbitrage, even though this is not benchmark arbitrage in the traditional sense. Most arbitrage situations are thought of as short term events. An index addition. An index deletion. A rebalance. A forced buyer. A forced seller. A gap that closes over days, weeks, or months.
This is different. This is structural. It may take years for the benchmarks to fully reflect the new AI economy. But the size and speed of the change make it feel like an event happening right now. The arbitrage is not that an index committee is about to make one adjustment. The arbitrage is that the real economy is already changing faster than the benchmark can evolve.
If the next decade is defined by AI CapEx, then today’s benchmarks are likely underweight the areas that matter most. They are underweight the physical inputs required to scale intelligence. This includes power, electrical infrastructure, advanced manufacturing, chemicals, optical connectivity, semiconductor equipment, packaging, and the fragmented industrial supply chains now sitting directly in the path of the AI spending wave.
This creates a rare moment. Investors can look at the world not as it is currently represented in the benchmarks, but as it may need to be represented ten years from now. That is benchmark arbitrage. It is a structural mismatch between where capital is currently allocated and where the physical economy must go.
The Cost of Underinvestment
The irony is that the prior software era helped create this opportunity. For years, capital flowed toward asset light businesses and away from asset heavy industries. Investors rewarded recurring revenue, high gross margins, buybacks, low capital intensity, and terminal value stories built on long duration cash flows. At the same time, many parts of the physical economy were neglected. Commodity capacity was underbuilt. Grid infrastructure aged. Industrial supply chains became optimized for cost, not resilience. Manufacturing was pushed offshore. Hardware became less fashionable than software.
Now AI is exposing the cost of that underinvestment.
The same investors who spent years rewarding companies for needing little capital now have to recognize that AI requires enormous capital. The winners are not only the companies deploying AI. They are also the companies selling the inputs needed for everyone else to deploy AI.
If every Fortune 500 company needs its own AI infrastructure, if every country wants sovereign AI, if every factory needs automation, if every car becomes an AI computer on wheels, if humanoids move from concept to production, and if every device becomes intelligent, then the bottlenecks will define the profits.
The receivers of the CapEx dollars become the new margin takers.
The Terminal Value Problem
This also explains why software has become more difficult to value. AI is disrupting the terminal value philosophy that supported many long duration assets. In the old model, investors could look three, five, or ten years out and assume that dominant software franchises would continue compounding with limited disruption. But AI changes the speed of competition. Coding is becoming cheaper. Intelligence is becoming more widely available. The cost of building software is collapsing.
That does not mean every software company fails. It means the durability of future margins is harder to underwrite. When the pace of change becomes exponential, the confidence interval around terminal value widens. A business that looked unassailable three years ago can suddenly face new competition from AI native workflows, agents, open source models, cheaper code generation, or a customer deciding to build internally rather than buy another software seat.
The market is beginning to understand that software may still be valuable, but the old assumptions about duration, pricing power, and defensibility need to be reexamined.
This is the other side of benchmark arbitrage. The old winners are not disappearing, but their dominance was built for a slower world. Their index weights reflect years of compounding in an era when software had scarcity value, code created durable moats, and terminal values could be modeled with more confidence. AI compresses that timeline. It questions the durability of some software margins at the same time it creates urgent demand for the physical inputs required to scale intelligence.
That is what makes the current moment so unusual. The market is not waiting ten years to recognize the strain. It is seeing it now across the physical supply chain. The indexes still carry the weight of the last era, but the bottlenecks are already pointing to the next one.
The Speed of Code Meets the Speed of Steel
Hardware and commodities face the opposite dynamic. They were ignored because they were messy, cyclical, capital intensive, and fragmented. But those are precisely the characteristics that can create opportunity when demand shocks arrive. If supply is slow to respond and demand accelerates, pricing power can emerge in unexpected places.
The world cannot prompt its way into more electricity. It cannot instantly create more transformers. It cannot magically permit new data centers, manufacture more high bandwidth memory, expand advanced packaging capacity, or produce the specialty chemicals required for leading edge semiconductors overnight.
The digital world moves at the speed of code. The physical world moves at the speed of steel, copper, silicon, chemistry, energy, and regulation.
That gap is the investment opportunity.
AI is forcing the fastest moving technology in history to collide with some of the slowest moving supply chains in the economy. That collision creates bottlenecks. Bottlenecks create pricing power. Pricing power creates earnings revisions. Earnings revisions eventually force benchmark weight changes. The investor’s job is to identify those changes before the benchmark does.
Follow the Bottlenecks
The old world was about concentration. The new world is about fragmentation. The Mag 7 captured the margin in the software age because software rewards scale, distribution, and network effects. The AI physical buildout rewards a much broader ecosystem. The winners may include semiconductor companies, packaging suppliers, memory producers, power equipment manufacturers, grid operators, industrial automation companies, thermal management firms, optical networking providers, specialty chemical companies, construction firms, and commodity producers.
The opportunity spreads across countries, sectors, and supply chains.
That does not mean every hardware or commodity company is a winner. It does not mean investors should blindly buy anything related to AI infrastructure. The point is more precise. The world is moving from a software dominated investment regime to a full stack AI buildout regime. In that world, the most attractive opportunities may appear in places that benchmarks still treat as secondary.
Investors need to stop viewing AI only through the lens of applications and start viewing it through the lens of constraints. Where is the shortage? Where is the bottleneck? Where is the underinvestment? Where does permitting take years? Where does supply require physical capacity? Where are the receivers of the CapEx dollars?
That is the new map.
This is also why I am currently building my thematic portfolio (https://ai.22vresearch.com/) around the five-layer AI cake. If the opportunity is moving from the software layer into the physical infrastructure layer, then active management has to evolve with it. The goal is not simply to own a static basket of AI winners. The goal is to understand where we are in the cycle, where the bottlenecks are forming, where capital is flowing, and how the weights should shift across the stack as the 90 trillion dollar buildout progresses. Early in the cycle, that may favor semiconductors, advanced packaging, power equipment, and optical connectivity. As the cycle broadens and commodity shortages and power constraints take move, the focus may move toward data centers, energy, chemicals, cooling, automation, and eventually applications, agents, and humanoids. Benchmark arbitrage only becomes actionable if it can be translated into portfolio construction, active reweighting, and a disciplined process for moving capital along the cake as the AI economy evolves.
The software era taught investors to follow margin capture. The AI era will teach investors to follow capital expenditure. The previous winners made money by using code to compress costs and attack incumbents. The next winners may make money because everyone else must spend capital to survive.
The world has changed. The opportunity has shifted. The benchmarks still reflect the last era, but the physical world is already being rebuilt for the next one.
Your margin was my opportunity.
Your CapEx is my opportunity.