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TLDR:
Our solar system holds a near-infinite supply of energy, metals, fuel, and real estate. It’s hard to fathom the degree of wealth that exists beyond the bounds of Earth. A single metal asteroid (e.g., 16 Psyche) carries an estimated $10,000
quadrillion
in metals. Carbonaceous asteroids hold the water that becomes hydrogen and oxygen (i.e. rocket fuel), the “oil of space.” Unlimited near-Earth sunlight is about to power a new industry: space-based AI compute, scaling from hundreds of gigawatts to terawatts once we build manufacturing on the lunar surface. The Moon and the asteroids are racing to become humanity’s first off-world mines. Five hundred years ago a handful of ships crossed an ocean and unlocked a “New World” that rewired the global economy. We’re standing at that same edge again. Except this time the ocean is space, and the New World has no shoreline.
Everything we value on Earth (energy, metals, minerals, real estate) is in near-infinite quantities in space. I’ve been saying for over a decade that the first trillionaires will be on the space frontier (in fact, one has already reached that threshold). This is not mere optimism for its own sake. It’s arithmetic.
“Abundance is our future. Scarcity is our past.”
— Peter
Let me show you the numbers behind that claim.
THE OCEAN WE ALREADY CROSSED
In 1492, Columbus crossed the Atlantic with three ships and a bad map. What followed was more than an exploration. It was the largest expansion of accessible resources in human history. New land, new metals, new crops, new trade routes. The flow of New World gold and silver into Europe is estimated to have moved hundreds of billions of dollars in today’s money, and it restructured the entire global economy.
The map is about to get bigger than any explorer in history could have imagined. And this time, there’s no edge to fall off.
Now here’s the part most people miss. The New World wasn’t more valuable than Europe because of any single mine. It was more valuable because it multiplied the surface area of human possibility. The map got bigger.
Space does the same thing, except the multiplier isn’t 2x or 10x, its one-million-fold bigger... it’s effectively unbounded.
METALS: A SINGLE ASTEROID WORTH MORE THAN THE GLOBAL ECONOMY
Start with the headline number. As mentioned above, a single asteroid named 16 Psyche, a metal-rich body about 140 miles across, has been valued at roughly
$10 quintillion
in metals. That’s a 10 followed by 18 zeros. For scale, the entire global economy is somewhere around $100 trillion. Psyche is on the order of 100,000 times larger than everything humanity produces in a year. Clearly such a quantity of metals would crater the metals market, but it would also increase the use of these materials (Jevon’s paradox).
Sources
: NASA / Newsweek (16 Psyche ~$10 quintillion); Goldman Sachs (single-asteroid platinum ~$50B); World Bank (global GDP ~$100T); World Gold Council (above-ground gold stock). Log scale.
Psyche is a nickel-iron asteroid, the exposed core of what may have been a baby planet. The reason it matters isn’t the iron. It’s what rides along with it: platinum-group metals.
Platinum today trades north of $1,800 an ounce. These metals are the catalytic backbone of hydrogen fuel cells, electronics, and clean energy hardware, and on Earth they’re brutally rare. On a metallic asteroid, they can be concentrated at levels that would make any terrestrial mine look like a sandbox.
FUEL: THE OIL OF DEEP SPACE
What’s more valuable than platinum-group metals (that are primarily used on Earth)? Rocket fuel, derived in space, for use in space, specifically water-ice mined from Carbonaceous chondrite asteroids. Run an electric current through that water and it splits into hydrogen and oxygen. Hydrogen and oxygen are rocket propellant. They’re also breathable air and drinkable water.
So, why does that matter so much? Because the single most expensive thing in spaceflight is hauling fuel up out of Earth’s gravity well. Every gram of rocket fuel used in space since 1957 has been lifted out of Earth’s gravity well. Lifting a kilogram of anything to orbit has historically cost on the order of tens of thousands of dollars. If you can refuel in space, from water mined off an asteroid, you’ve just removed the biggest tax on every mission to the Moon, Mars, and beyond.
Water is the oil of the deep-space economy. The first company to set up Earth and Moon orbiting refueling stations will own the most valuable service facilities humans have ever staked. Not because the water is rare. Because it sits at the chokepoint of everything else.
ORBITAL ENERGY & DYSON SWARMS
Early in my space-career (way back in the 1980’s), when I was envisioning future value creation in orbit, I imagined tourism or in-space manufacturing. I never imagined space-based AI satellites (humanity’s Dyson Swarm).
As Eric Schmidt has noted, the limiting factor for AI on Earth today isn’t chips or talent… it’s power. The grid is maxing out. In orbit, that constraint simply disappears.
Back in 1964, the Russian astrophysicist Nikolai Kardashev proposed ranking cosmic civilizations not by their morality or technology but by the energy they can harness. A Type I civilization captures all the energy available on its home planet, roughly 10^16 watts (for Earth). A Type II commands the full output of its star, on the order of 10^26 watts (for Sol), by wrapping a collection shell around the star, a Dyson sphere. That’s not a 10x jump over a planet. It’s a ten-billion-fold jump. The sun pours out more energy in one second than humanity has used in its entire history.
And what will we eventually do with 10^26 watts of energy? Power increasingly advanced AI systems.
COMPUTE: WHEN THE DATA CENTER MOVES TO SPACE
Here’s where the energy story turns into the most valuable industry of the century. If sunlight is free and unlimited in orbit, then the thing you most want to put up there is computation. AI compute is basically electricity converted into intelligence. Move the power source to space and you move the data center with it.
In the near-term, we’re not build a sphere around the Sun, but we are likely to launch hundreds of thousands of AI-data-center satellites into sun-synchronous orbits around the Earth.
Elon has laid out the math in two stages, and the numbers are worth sitting with.
Elon’s near-term prediction (by 2030):
A few hundred gigawatts per year of AI compute launched into space. Starship, he says, could deliver around 100 gigawatts per year to high Earth orbit within four to five years, scaling launch mass from about 2,500 tons a year today, to a million tons a year within by 2030. For scale, a single gigawatt is about the output of a large nuclear reactor, and the entire U.S. has on the order of a few hundred gigawatts of AI-capable power today. He’s talking about adding that much new compute capacity every year, from orbit, with no grid to fight and no cooling bill.
Elon’s longer-term prediction (2040’s):
Where next? Moving from 100 GW to 100 terawatt scale, built on lunar mining. In Elon’s words, “100 TW per year is possible from a lunar base producing solar-powered AI satellites locally, and accelerating them to escape velocity with a mass driver.” A terawatt is a thousand gigawatts. A hundred terawatts per year is on the order of the entire current power output of human civilization, added annually, in space.
Sources: Elon Musk (Dwarkesh Podcast, Feb 2026; SpaceX statements 2026) for ~100 GW/yr Starship-to-orbit, few-hundred-GW/yr near-term, and 100 TW/yr lunar-built long-term; US AI-capable power ~50 GW order-of-magnitude. Log scale.
Jeff Bezos said it plainly: “It’s 10 plus years, but I bet it’s not more than 20 years. We’re going to start building these giant gigawatt data centers in space. We will be able to beat the cost of terrestrial data centers in space in the next couple of decades.” Eric Schmidt bought the launch company Relativity Space to chase the same vision. Starcloud already flew the first NVIDIA GPU to orbit as a proof of concept, and Crusoe plans to stand up the first public cloud in space. When three of the wealthiest technologists on Earth independently bet on the same “impossible” idea, pay attention.
The sun pours out more energy in a single second than humanity has used in its entire history. We’re finally building the bucket.
REAL ESTATE: A FRONTIER WITH NO SHORELINE
When European settlers reached the Americas, the most enduring wealth turned out not to be the gold they shipped home. It was the land they settled (and/or took from the native populations). Every acre eventually became a farm, a city, a market.
Now apply that to space. The asteroid belt alone contains over a million asteroids larger than a kilometer. The Moon has 14.6 million square miles of surface, almost the area of Asia, sitting three days away. There is no Atlantic to cross, no edge of the map, no fixed quantity of “land” to fight over. The frontier keeps expanding the moment you reach it.
Jeff Bezos takes this to its logical conclusion. With the resources and solar power of the solar system, he argues, “We could have a trillion people out in the solar system,” and “if we had a trillion humans, we would have a thousand Einsteins and a thousand Mozarts, and unlimited, for all practical purposes, resources and solar power.” That’s the part that moves me most. Abundance doesn’t only mean more metal and more energy. It’s also more intelligence, both human minds and AIs. More intelligence that is free to invent, create, and solve. The ceiling on genius has always been the size of the human population and the technical resources to support it. Space lifts both.
Scarcity is a story we tell because Earth has edges. The solar system doesn’t.
“When you have an Abundance Mindset, rather than slicing the pie into thinner and thinner slices, you bake more pies.”
— Peter
This is the part that disrupts people’s intuitions. Every economic model we’ve ever built assumes finite resources and zero-sum competition. That assumption was always temporary. It was true only for as long as our reach ended at the atmosphere.
THE MOON VERSUS THE ASTEROIDS
So where do we mine first? This is the real strategic debate, and there are good arguments on both sides.
The Moon
is close. Three days away, with water ice locked in its permanently shadowed polar craters and helium-3 scattered across its regolith. Proximity is the Moon’s superpower. Lower launch costs, faster round trips, a natural staging base for everything deeper. The case against it: extraction is hard in one-sixth gravity, and the richest dreams (large-scale helium-3 for fusion) depend on fusion reactors we don’t yet have.
The asteroids
are richer and more varied, unincumbered by a significant gravity well. Metallic asteroids concentrate platinum and nickel-iron at grades no planet’s crust can match, and carbonaceous ones hand you fuel and water. The case against: they’re far, they’re scattered, and a mission is a multi-year commitment.
My honest take, after fifteen years of thinking about this: it’s actually not either-or. The Moon is the training ground and the gas station. The asteroids are the motherlode. We learn to mine on the Moon because it’s close, then we apply what we learn to the asteroid belt because that’s where the real wealth lives.
Even some longtime lunar advocates now admit the return on asteroids may beat the return on the Moon.
THE LUNAR ECONOMY: OUR FIRST OFF-WORLD ADDRESS
Everything I’ve described needs a beachhead, and the Moon is it. Three days away, it’s close enough to be our practice field, our fuel depot, and our first factory floor off Earth. This is no longer a thought experiment. NASA’s Artemis program, China’s lunar plans, and a swarm of private landers are all converging on the same gray dirt this decade.
The bankers have started doing the math. PwC projects a lunar economy worth roughly $170 billion a year by 2040, built on three pillars: government exploration contracts, lunar surface operations, and the mobility and logistics to move between them. That’s before the truly large prize, which is using lunar water ice for propellant and lunar metal for manufacturing, so we stop shipping everything up from Earth.
Sources: PwC Lunar Market Assessment (~$170B by 2040); SpaceNexus cislunar segment estimates; 2050 figure reflects illustrative continued build-out. Log scale.
Here’s the strategic insight that gets overlooked. The Moon’s real value isn’t what we ship back to Earth. It’s what we never have to ship up from Earth again. Once you can make rocket fuel, structural metal, and solar panels on the lunar surface, the Moon becomes the assembly line for everything deeper in the solar system.
Remember Elon’s terawatt vision: solar-powered AI satellites manufactured on the Moon and flung into deep space by an electromagnetic mass driver, no rockets required. The lunar economy and the orbital-compute economy are the same story told from two ends.
WHAT THIS MEANS FOR YOU
If you’re an entrepreneur:
The picks-and-shovels opportunities here are wide open. Refueling depots, extraction robotics, ISRU processing, orbital logistics. You don’t have to own an asteroid to get rich serving the people who do.
If you’re an executive:
Start tracking the cost-per-kilogram-to-orbit curve the way you track Moore’s Law. When it crosses certain thresholds, entire industries (metals, energy, manufacturing) get a new and effectively unlimited supply chain.
If you’re an investor:
There will be many first trillion-dollar fortunes made in space. Fuel and logistics before glamour.
If you’re a student:
This is the field of your lifetime. Planetary science, robotics, materials, space law. The people who write the rules and build the tools for the off-world economy are in school right now.
If you’re a parent:
Teach your kids that the future is not a fixed pie to be divided. It’s an expanding frontier to be built. The mindset of abundance is the most valuable thing you can hand them.
FIFTY YEARS, ONE MAP
Put it all on a single timeline and the shape of the century comes into focus. Satellites and launch are the mature core today. Space tourism is the luxury edge already selling seats. Orbital AI data centers begin this decade and become the largest line on the chart. Lunar infrastructure matures through the 2030s and 40s. Asteroid mining, the business I bet on too early, finally arrives once the fuel depots and lunar factories make it cheap to reach the belt.
Illustrative build-out. Near-term anchors: McKinsey/WEF total space economy ~$1.8T by 2035 (from $630B in 2023); Morgan Stanley >$1T by 2040; PwC lunar ~$170B by 2040; space mining ~$40B and space tourism ~$10–90B by 2035. Figures beyond ~2040 are directional extrapolations, not forecasts.
McKinsey and the World Economic Forum already peg the total space economy at $1.8 trillion by 2035, nearly triple its 2023 size. And that estimate barely accounts for the orbital-compute and asteroid-mining curves that bend skyward in the decades after. The trillion-dollar sky isn’t one industry. It’s a stack of them, each one unlocking the next.
On June 12, 2026, SpaceX went public in the largest IPO in history and Elon became the world’s first trillionaire. In his remarks, he repeated the line he’s been saying for 24 years: the goal is to “take the fiction out of science fiction.” That’s exactly what’s happening to everything in this newsletter. My early predictions came true… The first trillionaire was made in space. He just won’t be the last.
“The best way to predict the future is to create it yourself.”
— Peter
The only question that matters is whether you still believe scarcity is permanent. Look up, and ask yourself how much of that sky you’re willing to call impossible.
To a future of Abundance,
Peter
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TLDR:
Radical transparency is coming. A future where you can know anything, anytime, anywhere. A future where no one can hide. We are wrapping the planet in an "Sensor Ecosystem": a living, multi-layered sensing system that runs from the cameras in your home, to the phone in your pocket, to autonomous cars and humanoid robots on the ground, to drones and flying cars in the air, all the way up to a constellation of satellites imaging every square meter on the Earth every single day. By 2030, there will be roughly 40 billion connected devices, each packing multiple sensors, generating a flood of data that AI can finally read. In Part 1 of this newsletter I’ll show you the scale of what’s being deployed, layer by layer. In Part 2, I’ll share what it means, because when everything becomes visible, it gets very hard for anyone to hide, and that changes how humans behave.
PART-1: THE TRILLION-SENSOR ECOSYSTEM
Earlier this week, I hosted a conversation with Will Marshall, CEO of Planet, on my
Moonshots
podcast. Planet operates the largest fleet of Earth-observing satellites in human history and Will walked me through what’s coming. We are constructing a planet-scale sensing organism, and most people have no idea how big it’s already gotten.
OUR ELECTRIC SKIN, LAYER BY LAYER
Layer 1 – Space: Planetary Intelligence from Orbit
There are roughly 13,800 active satellites in low Earth orbit today. Analysts at consulting firm Analysys Mason predict that this number will climb to somewhere between 27,000 and 100,000 by 2030. SpaceX’s Starlink alone has about 10,400 operational satellites today, on the way to a licensed 42,000.
But the satellites that matter most for transparency are the ones that image
us
. Planet runs about 200 of them, collecting 25 terabytes of imagery a day and a 150-petabyte archive, roughly 3,000 images of every point on Earth’s land surface over the past decade.
And it’s getting sharper. Planet’s next-generation Pelican satellites, launching in late 2026, will hit 30-centimeter resolution with a 32-satellite constellation revisiting any point on Earth up to 10 times a day, delivering imagery in 30 minutes.
Layer 2 – The Air: Drones and Flying Cars
Drop down from orbit into the atmosphere and you hit the aerial layer. The FAA already has more than a million drones registered in the United States. Globally, the commercial drone market is headed past $50 billion by 2030, with Asia-Pacific alone projected to field around 9 million commercial units.
Delivery is where it gets wild. The number of package-delivery drones (each with a suite of cameras and sensors) is forecast to jump from about 32,000 in 2024 to over 275,000 by 2030 (MarketsandMarkets). Inspection and survey drones routinely resolve the ground at 1 to 3 centimeters, sharper than any satellite. And above them, the eVTOL “flying car” layer is arriving:
Aviation Week
projects roughly 2,000 eVTOLs delivered by 2030, scaling into a market Morgan Stanley once pegged at over $1 trillion by 2040. Every one of these aircraft carries lidar, radar, and electro-optical cameras to navigate. They don’t just fly, they
see
.
Layer 3 – The Road: Autonomous Vehicles
Now we’re at street level. Waymo runs about 3,000 robotaxis today, delivering 500,000 paid rides a week. Goldman Sachs projects roughly 35,000 robotaxis on US roads by 2030, with global autonomous vehicle counts running into the tens of millions.
Here’s what stops me cold. A single Waymo vehicle carries 13 cameras, 4 lidar units, and 6 radar, with 17-megapixel imagers that out-resolve almost anything in consumer hardware. One autonomous car generates on the order of 4 terabytes of data per hour of driving. Recent engineering analysis puts the lidar stream alone at up to 8 petabytes per year, per vehicle. Multiply that by tens of thousands of cars, each mapping every street, every pedestrian, every pothole, in real time. We are accidentally building the most detailed, continuously updated map of the physical world that has ever existed. And down on the sidewalk, ground-delivery robots (e.g., Starship already runs about 2,700, heading toward 12,000) are filling in the gaps the cars miss.
Layer 4 – The Walking Layer: Humanoid Robots
This is the newest layer, and it may become the densest. Humanoid robots are about to walk through our factories, our streets and our homes. And every one of them is a mobile sensor platform with eyes.
I’m going to use the builders’ own numbers here, not an analyst’s. Elon has said he’s confident Tesla can reach 1 million Optimus units per year by around 2030. Figure has built BotQ, a factory designed to produce up to 12,000 humanoids a year on its first-generation line, roughly one robot per hour. Both Elon and Brett Adcock’s long-term vision predict >10 billion humanoids globally.
Each of these machines is vision-first: multiple onboard cameras, inertial sensors, force and torque sensors in every joint, tactile sensors in the hands, all capturing continuously. A humanoid working an 8-hour shift is plausibly generating data on the scale of an autonomous car. Now picture millions of them, then tens of millions, walking through the world and recording it.
Layer 5 – The Pocket: 7 Billion Smartphones
The layer you’re holding right now. There are about 7 billion smartphone subscriptions on Earth, and the average phone carries somewhere between 14 and 20 sensors: multiple cameras, GPS, accelerometer, gyroscope, magnetometer, barometer, microphones, proximity and light sensors. This is, by a wide margin, the largest sensor network humanity has ever deployed, and we did it by accident, one upgrade at a time. Every person becomes a roving, multi-spectral data node.
Layer 6 – The Ground: Home Cameras & Industrial IoT
Finally, the ambient layer woven through our buildings, streets, and homes. There are already over 1 billion surveillance cameras in use worldwide. Add the Ring doorbells, the Nest cams, the Arlo and Wyze units multiplying on every porch. Then add the industrial layer: the sensors on pipelines, factory floors, shipping containers, and power grids.
Tie it all together and you get the number that frames everything: total connected IoT devices stand at about 21 billion today and are projected to reach roughly 40 billion by 2030, each one carrying multiple sensors. That’s where the “trillion sensor” world stops being a metaphor.
Sources: Planet Labs; FAA; MarketsandMarkets; Goldman Sachs; Waymo; Tesla; Figure AI; Ericsson; IoT Analytics; Video Experts Group (2024–2026)
THE EXPONENTIAL, IN ONE NUMBER
Stack the layers and the trajectory is unmistakable. As of 2025 we had about 21 billion. By 2030, around 40 billion. By 2040, as humanoids, autonomous fleets, and orbital constellations compound on top of everything else, we move from billions of devices into the realm of trillions of individual sensors, each one streaming a slice of reality into AI systems that can finally make sense of all of it.
Source: IoT Analytics (2025); The Future Is Faster Than You Think (Stanford/Accenture)
That’s the engine. Now here’s why it changes everything about human behavior.
PART-2: WHAT HAPPENS WHEN NO ONE CAN HIDE
During my conversation with Will Marshall, he began to layout the consequences:
“No one can hide anymore,” he said. “If you build a school, we’re going to see the school. If you build a data center, we’re going to see the data center. And the accountability is going to be there for the whole world to see, no matter what.”
And the human behavioral consequences are profound. When people know they’re being watched, they behave different, they behave better. And we now have hard data proving it.
WHEN PEOPLE KNOW THEY’RE WATCHED
The cleanest evidence comes from one of the best natural experiments in social science: police body cameras.
In 2012, the police department in Rialto, California ran a randomized controlled trial with Cambridge criminologists. Over the course of a year, officer shifts were randomly assigned “camera on” or “camera off,” collecting more than 50,000 hours of police-public interactions. The result was use-of-force incidents dropped 60%, and citizen complaints against officers fell 88%, from dozens the prior year down to a grand total of three.
Source: Rialto, CA randomized controlled trial — Ariel, Farrar & Sutherland, Cambridge (2015)
Researchers call it the “civilizing effect.” When both the officer and the citizen know the encounter is on the record, both sides regulate themselves. Nobody wants to be the person on the tape. Cambridge has since replicated the work across ten more forces, and the core finding largely holds.
“When both sides know they’re on the record, both sides behave better. Sunlight is becoming infrastructure.”
And it’s not just cops. The definitive 40-year meta-analysis of closed-circuit television, led by Eric Piza with Brandon Welsh and David Farrington, found CCTV associated with about a 13% reduction in crime overall, and a much steeper drop, over a third, in settings like car parks.
Source: Piza, Welsh, Farrington & Thomas — 40-year meta-analysis, Criminology & Public Policy (2019)
The pattern runs all the way up to governments. A 2023 review of 114 studies in
Public Administration and Development
found that transparency and disclosure measurably reduce corruption, especially when citizens can act on what they learn. Alina Mungiu-Pippidi has shown that in countries with weak rule of law, financial disclosure and freedom-of-information access work as a substitute for honest institutions. Citizens become the enforcement mechanism when the courts won’t be.
TRANSPARENCY AS A DETERRENT TO WAR
Will Marshall next argued that transparency doesn’t only civilize individuals. It actually can prevent wars. “Throughout history,” he said, “wars happened mainly when there’s been misinformation, a lack of information, and people had to guess or made mistakes. Transparency drives accountability and reduces the probability of war.”
He gave a concrete example. In the run-up to the invasion of Ukraine, Vladimir Putin massed troops on the border believing the buildup would go unnoticed. Planet’s imagery put it on the front page of every newspaper on Earth. “Putin thought he could get away with it,” Will said. “We put that to bed.”
It didn’t stop the invasion. But it changed the information environment around it. For most of human history, the fog of war was literal: leaders started wars because they couldn’t see what the other side was doing, and they guessed wrong. We’re now building a world where everyone can see the troops massing and the treaties breaking, in near real time. You can monitor a peace accord from orbit. You can’t claim the bridge is standing when 200 satellites photographed it fall.
Governments own the map.
But Planet owns the sky above the map.
THE OTHER EDGE OF THE BLADE
I’d be doing you a disservice if I sold you only the utopian version. An electric skin that registers everything is also an electric skin that registers you and can dissolve privacy.
The hard truth is that transparency is a tool, and tools don’t have ethics. A surveillance state and an accountable democracy can run on the identical sensor network. What separates them is who can see, who is seen, and whether the watching runs in both directions. The world I worry about isn’t the one where everyone is visible. It’s the one where the powerful can see everyone, while no one can see them. Transparency only builds trust when it points both ways.
WHAT THIS MEANS FOR YOU
If you’re an entrepreneur:
Assume your operations, supply chain, and claims are about to become independently verifiable by anyone with an API key. Build the company you’d be proud to have photographed from orbit or an overhead drone. The brands that win the transparency era are the ones that were already telling the truth.
If you’re an executive:
Radical transparency is a moat, not a threat… if you move first. Open your data, your sourcing, your numbers, before a satellite or a sensor does it for you. Disclosure you volunteer builds more trust than disclosure that’s extracted from you.
If you’re an investor:
A new asset class is forming around verifiable ground truth: counting cars in lots, ships in ports, construction in progress. Alpha is shifting to whoever can see the physical world in real time. Planet (PL) is one way to play it, and there will be many more.
If you’re a student:
The most valuable skill of the next decade is turning oceans of sensor data into honest answers. Learn to work where AI meets the physical world. For the first time, the library is being connected to the window.
If you’re a parent:
Your kids will grow up in a world with no “off the record.” Teach them that the best privacy strategy is integrity, living so that being seen costs you nothing. And fight, hard, for a world where the watching goes both ways.
Will Marshall is building the crystal ball. The sensors are going up by the billions, and they are not coming down. So here’s the question I’ll leave you with, the one I’ve been chewing on since we turned the mics off: in a world where no one can hide, do we become our better selves because we choose to, or only because we’re being watched?
To a future of Abundance,
Peter
P.S. How much is an extra decade of healthy life worth to you?
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Abundance Longevity Trip
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I personally host and guide 80 Members each year. Investors come for the pre-market deal flow. Researchers and practitioners come for the latest science. Others come for personal health breakthroughs. Everyone leaves with relationships and insights that change how they think about their future.
Secure one of the final 3 seats.
Peter
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Elon described the near future as a “supersonic tsunami”: a wave moving so fast and so large that by the time you hear it coming, it has already broken over you. The phrase stuck with me. So let me lay it out in detail, with the actual numbers, because I don’t think most people have any real sense of what the next 60 months hold.
Buckle up.
GENIUS FOR LESS THAN A CUP OF COFFEE
Start with the price of intelligence, because it is collapsing faster than anything in the history of technology. According to the Stanford AI Index, the cost of tokens dropped
280x collapse in 24 months
. For frontier models, the price has been dropping about
10x every single year
, from $20 to about $0.40 per million tokens. Not 10% cheaper. Ten times cheaper, annually.
“We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence.”
— Sam Altman, OpenAI
Source: Stanford AI Index 2025 (GPT-3.5-level inference, $/million tokens).
Now stack that against the AGI race itself. Dario Amodei has said a system amounting to “a country of geniuses in a datacenter” could come online as early as
late 2026 to 2027
. Elon predicts AGI before the end of this year. Whatever month it lands, the meaningful event is recursive self-improvement: AI that designs better AI, on a loop, with each turn faster than the last.
Soon thereafter comes artificial superintelligence (ASI): a single system more capable than
the combined intellectual output of all of humanity
, across every domain at once. Elon told me he expects “digital intelligence to exceed the sum of all human intelligence by around 2031.” One mind, smarter than eight billion of us put together, available for pennies.
The implication is brutal and beautiful at once: every knowledge job built on “I know something you don’t” gets repriced overnight, while every founder, researcher, and dreamer suddenly commands a research staff of a thousand PhDs for the price of lunch. The moat stops being what you know and becomes what you choose to point that intelligence at.
“It blows my mind multiple times a week. Just when I think ‘wow,’ two days later, more wow. Exponential wow.”
— Elon Musk, on the Moonshots podcast
THEN AI SOLVES EVERYTHING…
Point that intelligence at the hardest open problems we have: math, physics, chemistry and biology. As my Moonshot Mate Alex Wissner-Gross says, “we are about to speed-run every science fiction movie and solve-everything.” AI already proves theorems, predicts the structure of 200 million proteins, and designs novel molecules from scratch. Now run it 1,000x faster and 280x cheaper.
We are about to compress centuries of discovery into a handful of years: room-temperature superconductors, new battery chemistries, materials that don’t exist in nature, drugs designed atom-by-atom for a single patient’s tumor. Every one of those breakthroughs creates wealth. It saves a life, extends a life, or quietly turns last year’s miracle into this year’s Tuesday. The discoveries will arrive faster than any of us can read the headlines announcing them.
“After powerful AI, we will make all the progress in biology and medicine in a few years that we would have made in the whole 21st century.”
— Dario Amodei, Anthropic (“Machines of Loving Grace”)
Here’s the implication: the bottleneck on progress flips. For all of human history, the scarce resource was brainpower (i.e., enough brilliant minds, enough time, to chase down a hypothesis). When that becomes infinite and nearly free, the constraint moves to the physical world: how fast can we run the experiments, build the reactors, fabricate the chips. Atoms become the bottleneck, not ideas. The winners of the next decade will be the people who can move atoms as fast as AI moves bits.
HOLLYWOOD IN YOUR POCKET, AND A CONVERSATION WITH
ANYONE
Within the next two years you’ll stream a full feature film generated on demand (your mood, your cast, your language) for the cost of a search query. The marginal cost of a blockbuster falls toward zero.
Stranger still, you’ll sit down with anyone. Einstein to walk your daughter through relativity. Marcus Aurelius for a 2 a.m. talk on how to live. A living celebrity rendered so faithfully you forget it’s software. The line between a real person and a digital persona blurs to the point that “AI personhood” stops being a sci-fi punchline and becomes a question courts and legislatures actually have to answer.
And this reaches past entertainment. A child anywhere on Earth gets a patient, brilliant tutor that costs nothing, the greatest equalizer in the history of education. But you can no longer trust that the face on your screen is real, and “Who owns your likeness after you die?” becomes a live legal fight. Abundance and disruption, on the same wave.
THE ROBOTS ARE COMING HOME: BY THE HUNDREDS OF MILLIONS
Here is where the numbers get truly strange. A Tesla Optimus is targeted to cost
$20,000
at scale; Tesla is openly scaling to build
one million units a year
. 1X’s Neo is priced at $20,000, or $499 a month. Unitree’s G1 already sells for about $13,500. Analysts expect capable consumer humanoids at
$10,000–$20,000 by 2030
.
Source: Elon Musk projection (100M–1B humanoids by 2031); illustrative ramp.
Financed over five years, a $20K robot runs roughly
$300 per month… $30 per day, well under a dollar an hour
for a machine that never sleeps. Elon’s projection:
100 million to 1 billion humanoid robots by 2031
. The intelligence inside them is the same frontier model collapsing in price above, so your robot won’t just fold laundry. It will cook like a Michelin chef working from ten thousand recipes, conduct a surgery with sub-millimeter precision, tutor your kids, and care for your aging parents with patience no exhausted human can sustain at 3 a.m.
“In three years, at scale, there will be more Optimus robots that are great surgeons than there are surgeons on Earth.”
— Elon Musk, on the Moonshots podcast
The result is the biggest labor shift since the tractor emptied the farms. Physical work, the thing that has defined the human economy since we stood upright, starts trending toward free, toppling the cost of building, manufacturing, and caregiving. The same robots that displace a task give a billion people their hours back. The hard question every society now has to answer: when work is optional, where do meaning and dignity come from?
YOUR BODY BECOMES EDITABLE CODE
This is the part I care about most. Biology is becoming readable, then
writable,
something we debug, patch, and rewrite. Disease stops being fate and becomes an engineering problem. Aging itself moves onto the list of things we can slow, halt, and one day reverse.
I watch the early version of this every day at Fountain Life, the company I co-founded to catch disease before it catches you. Roughly two of every ten members walk in feeling perfectly healthy and walk out with a life-saving diagnosis: a stage-1 cancer, an aneurysm quietly waiting to kill them. That’s
today’s
technology, before AGI. Add five years of superintelligence on top and “your healthspan” (the single most valuable asset you own) gets the upgrade of the century.
“I think we can cure all disease with the help of AI. The end of disease is within reach, maybe within the next decade.”
— Demis Hassabis, Google DeepMind / Isomorphic Labs
The outcome is the one I care about most: longevity escape velocity (LEV) moves from a slide in my keynote to a planning assumption for your life. If we can add more than a year of healthy life for every year you stay alive, then your single most important job right now is simply to not die of something stupid in the interim. To make it to the next breakthrough, and the one after that. My mentor and friend Ray Kurzweil predicts we will reach LEV by 2033. Health stops being the thing you spend wealth on and becomes the foundation that lets you enjoy all the rest.
“A doubling of the human lifespan is not at all crazy, and with AI we may be able to get there in five to ten years.”
— Dario Amodei, Anthropic
THE SKY AND THE STREETS, REINVENTED: AT 20 CENTS A MILE
Look out the window. A human rideshare today costs you roughly
$2.00 a mile
. Cathie Wood, ARK Invest, projects a Waymo will run about
40 cents a mile by 2030, and Tesla’s purpose-built Cybercab closer to 20 cents
. A 10x cut that makes owning a depreciating car parked 95% of the day look absurd.
Above the gridlock, eVTOLs (flying cars, finally real) are crossing from prototype to certified product, turning the empty sky over our cities into open highway. Drones drop our packages, inspect our bridges, and rewire global logistics. Cheap, ubiquitous, autonomous movement on the ground and in the air will rebuilds where we live, how far we’ll commute, and what a city even is.
The implication ripples straight into the largest asset class on Earth: real estate. When a 60-mile commute costs a few dollars and you can read, sleep, or work the whole way, the premium on living near the office evaporates. Land 90 minutes out becomes 20 minutes up. The parking lots and garages that swallow a third of every city center are freed to become parks. We will quietly redraw the map of where humans choose to live.
Source: ARK Invest cost-per-mile projections, 2030 (via
Top 10 Metatrends Report
).
THE ECONOMY GOES VERTICAL
Now for the number that stops people in their tracks. When I interviewed Elon on the
Moonshots
podcast, he told me he expects something on the order of a
10x expansion of global GDP within ten years
: a world economy climbing past
a quadrillion dollars
, with the doubling period collapsing from decades to a handful of years.
I’ve known Elon for 26 years. I’ve watched smart people bet against his vision for two decades. They keep losing. So when he throws out a number like that, I don’t laugh. I run the math, and I hand it to the young founders I mentor: if global GDP is heading toward $1.2 quadrillion and only a small handful of people are doing genuinely foundational work, your personal quota (the value you’d need to create just to keep pace) lands near
$10 billion
. Watching that number register on a 25-year-old’s face in Dave Blundin’s Link Studios is one of my favorite things in the world.
“In the next 18 months we hit 10% GDP growth, and by 2030, triple-digit: 100% GDP growth.”
— Elon Musk, on the Moonshots podcast
This is the engine under everything else: free intelligence, plus tireless robots, plus discovery on fast-forward. This does more than merely improve life. It manufactures wealth at a scale the species has never seen, with no natural ceiling in sight. The hard part was never creating the Abundance. It’s deciding how widely we share it.
“If AI becomes advanced enough to run companies, why not my own? I should be the most willing to do that.”
— Sam Altman, OpenAI
The upshot is the defining political and moral question of the next decade. A quadrillion-dollar economy can lift everyone or concentrate in a few hands. The technology is indifferent… the choice is
ours
. I’m betting on broad Abundance, because for the first time the size of the pie is effectively unbounded, and a world of empowered, healthy, optimistic people is simply a better market, a more stable society and a better place to live. But it won’t happen by accident. We have to build it on purpose.
WHAT THIS MEANS FOR YOU
If you’re an entrepreneur:
point superintelligence at a trillion-dollar problem, not a feature. Your competitive moat is no longer knowledge. It’s the audacity of the problem you choose and the speed at which you move atoms.
If you’re an executive:
assume the cost of cognition and physical labor both trend toward zero inside five years, and rebuild your org chart around that. This is the start of what Salim Ismail calls the “organizational singularity.” The companies that win will retool now, not after their competitors already have.
If you’re an investor:
the value is migrating to whoever owns the bottleneck: energy, compute, robots, and the physical capacity to build. Bet on the picks and shovels of a quadrillion-dollar economy.
If you’re a student:
stop memorizing what a machine knows better, and master the things it can’t hand you: taste, judgment, the ability to ask the right question and rally humans around an answer. Learn to direct intelligence, not compete with it.
If you’re a parent:
your kids will grow up in a world heading towards Star Trek, with a tutor smarter than any professor and a robot in the home. Raise them curious, purpose-driven, adaptable, and kind. And keep them and yourself healthy long enough to ride this wave all the way out.
RIDE THE WAVE
“It’s a supersonic tsunami. You have to surf on top of it, or be crushed by it.”
— Elon Musk
Genius for forty cents. ASI by 2031. A billion robots for under a dollar an hour. Transport at 20 cents a mile. Aging on the list of solvable problems. A quadrillion-dollar economy.
The wave is already here. It’s loud, fast, and not waiting for permission. So I’ll leave you with the only question that matters: are you on the beach watching it come, or out on the board, paddling hard, ready to ride?
To a future of Abundance,
Peter
P.S. How much is an extra decade of healthy life worth to you?
The
Abundance Longevity Trip
(October 7–11, San Francisco) is five days of access you can’t get anywhere else: private visits to leading research labs, direct conversations with the scientists and biotech founders shaping the longevity revolution, and personalized diagnostics and therapeutics most people don’t know exist.
I personally host and guide 80 Members each year. Investors come for the pre-market deal flow. Researchers and practitioners come for the latest science. Others come for personal health breakthroughs. Everyone leaves with relationships and insights that change how they think about their future.
Secure one of the final 3 seats.
Peter
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The more time I spend helping people learn how to use AI, the more convinced I am that success with AI is less about technical ability and more about mindset. To borrow from Ralph Waldo Emerson, a line I have often used with my children about life, AI is a succession of lessons that must be lived to be understood. You do not really learn it by reading instructions. You learn it by running into roadblocks, adjusting, and trying again.
The people who are moving fastest are often the people with little knowledge of how a computer works. They are not always the youngest. They are not always the ones with the most impressive resumes. The people who seem to adapt best usually have something else: an entrepreneurial mindset.
They are willing to experiment. They are willing to get stuck. They are willing to be wrong. They are willing to treat roadblocks as information.
That last part matters.
Many people still approach AI with an assembly-line mindset. They want exact instructions. They want step one, step two, step three. They want the machine to behave predictably. They want to know that if they follow the recipe, the cake will come out exactly the same every time.
That is understandable. Most of us were trained that way. School, work, and institutions often rewarded the person who found the answer, followed the process, and completed the assignment.
AI asks for a different mindset.
AI is probabilistic. It predicts, generates, and weighs possibilities. It gives you an answer based on probabilities, context, training data, your prompt, and the patterns it believes are most likely to be useful.
This creates frustration for some people. They ask a question, get an imperfect answer, hit a roadblock, and conclude the tool failed. They ask the same question twice and get two different answers.
Others react differently. They look at the same roadblock and ask, “What did I just learn?”
Those are the people who will win.
Because AI is a probability machine. To use a probability machine well, you need a probability mindset.
For me, that mindset comes from three places: Annie Duke and thinking in bets, horse racing and emerging markets as Bayesian training grounds, and a lifelong love of Sherlock Holmes, mysteries, and the art of observation.
Put those together and you get the operating system I believe people need for the AI age.
Think in bets.
Update like a Bayesian.
Observe like Sherlock Holmes.
Thinking in Bets
I have written about Annie Duke many times because her work has had a lasting impact on how I think about decisions, uncertainty, and life. Her book
Thinking in Bets
is one of those books I believe everyone should read.
My connection to Annie’s ideas is personal. I have strong memories of sitting and talking to her about probabilities, poker, work, teaching, and the ups and downs of life. It reinforced a lesson that becomes more important every year: the world gives us incomplete information, hidden variables, randomness, and feedback. Most importantly, because everything is probabilistic, going back and becoming obsessed on decisions that had a different outcome than you hoped, wastes time and energy. Expect losses and move on.
That is poker. That is investing. That is parenting. That is entrepreneurship. That is life.
And now, that is AI.
The central lesson of
Thinking in Bets
is that we should separate decision quality from outcomes. A good decision can lead to a bad outcome. A bad decision can lead to a good outcome. The result does not always tell you whether the process was right.
That idea is critical for AI.
When someone uses AI once, gets a bad response, and says, “This is useless,” they are judging the entire process from one hand of poker. One output becomes the full verdict.
But one output is just information.
Maybe the prompt was too vague. Maybe the model needed more context. Maybe the user needed to ask for options before asking for a conclusion. Maybe they needed a different role, a better example, a clearer constraint, or a second model to judge the first model’s response. I wrote maybe here to be nice.
In my experience, I always assume these things for my prompts.
The better AI user says, “That answer showed me how to improve the next prompt.”
That is thinking in bets.
Every prompt is a wager. You are betting that this question, framed this way, with this context, will move you closer to a useful result. Sometimes it does. Sometimes it does not. The skill is improving your odds with each attempt.
This is why the entrepreneurial mindset matters so much.
An entrepreneur expects the first version to be a test. The first product is a prototype. The first customer reaction is feedback. The first obstacle is data. The first failure becomes part of the project.
That is exactly how people need to use AI.
AI rewards the person who keeps playing the hand intelligently.
The Bayesian Advantage
The second part of the AI mindset is Bayesian thinking.
Bayesian thinking sounds complicated, but the practical meaning is straightforward: you update your beliefs as new information arrives.
You start with a view. New evidence comes in. You adjust. The stronger the evidence, the more you adjust. The weaker the evidence, the less you adjust.
I was trained in this long before AI.
One of my earliest forms of training in probability was handicapping horse races. Horse racing is a brutal but beautiful classroom for uncertainty. You study the form, the track, the pace, the jockey, the trainer, the distance, the weather, and the odds. You form a view. Then new information changes the picture. A horse looks different in the paddock. The odds move in the last minutes. The track changes. A pace scenario becomes more or less likely due to weather or a scratch.
You are constantly updating.
Then I began my career trading emerging markets, which may be one of the greatest Bayesian training grounds in finance. In emerging markets, each day can feel like a month. Political events, currency shocks, liquidity gaps, policy changes, capital flows, rumors, and surprises all hit at once. The world moves faster than your model.
You learn quickly that rigid thinking is dangerous.
You need a view, and you need the ability to adjust the view. You need conviction, and you need to know what would change your mind. You need confidence, and you need humility in the face of new evidence.
That is the Bayesian muscle.
AI requires the same muscle.
In the old world of work, going in the wrong direction was expensive. If you wrote the wrong report, built the wrong deck, created the wrong spreadsheet, or started the wrong project, you could lose hours, days, or weeks. Because the cost was high, people became defensive. They wanted certainty before starting. They wanted approval. They wanted the perfect plan.
AI changes that.
The cost of going in the wrong direction has collapsed.
You can draft the memo, test the argument, build the outline, generate the code, summarize the research, create three versions, and compare them quickly. If the first direction is weak, you pivot. If the second direction is better, you update. If the third version reveals something you had not considered, you follow the signal. I use five different models on the same research and narrow it down based on the new information.
That is the real unlock.
AI makes exploration cheap.
When exploration becomes cheap, the best users are the ones who update fastest during the process.
They say, “That taught me something.”
They say, “Let’s test another direction.”
They say, “This output is not right yet, but it tells me what to ask next.”
They say, “My original idea was X, but after comparing X, Y, and Z, I now think Y has better odds.”
This sounds simple, but it is not how most people were trained. Schools and companies often reward answers, completion, and confidence. AI rewards adaptive intelligence.
The person who can say, “Here is my current hypothesis, but let’s test it,” is going to outperform the person who needs every step defined before beginning.
The person who can say, “This draft is raw material,” is going to improve faster than the person who stops at the first roadblock.
The person who can say, “The evidence changed, so my view changed,” is developing the exact mindset AI demands.
That is how intelligence works under uncertainty.
That is how an AI mindset is created.
The Sherlock Holmes Problem
The third piece of the AI mindset comes from another long-running personal obsession of mine: Sherlock Holmes, mysteries, and the art of observation.
I have always loved mysteries because they are really stories about information. The facts are there, but not all facts matter equally. Some clues are signal. Some are noise. Some are distractions. Some look irrelevant until the whole case turns around them.
The detective’s job is to notice what matters.
That is also the job of the AI user.
AI gives you abundance. More answers. More summaries. More drafts. More charts. More ideas. More angles. More arguments. More possibilities.
But abundance creates a new problem: filtration.
When information was scarce, access was the advantage. In the AI world, access is becoming less scarce. The advantage moves to judgment. Can you tell which output is useful? Can you see which line contains the insight? Can you spot the assumption that does not hold? Can you identify the missing variable? Can you recognize when the model is being fluent but shallow?
This is where the Sherlock Holmes mindset matters.
Holmes solves cases by observing each situation as unique. He looks for the clue that does not fit. He pays attention to the small detail everyone else ignores. He avoids forcing every mystery into the shape of the last mystery he solved.
That is an important lesson for AI.
A lot of people want universal prompts. They want one magical formula. They want one process that works every time. But AI works best when you become more observant.
What is this specific problem asking for?
What context does the model need?
What role should it play?
What information is missing?
What would a good answer look like?
What would make this answer wrong?
What clues in the output suggest the model misunderstood?
That is the observationalist approach.
The observational user wants understanding. The observational user says, “Let me understand what is happening so I can decide what to do next.”
That difference compounds.
You are building a relationship with AI.
The Entrepreneurial Mindset Wins
This is why I keep coming back to the entrepreneurial mindset.
Entrepreneurs are used to uncertainty. They are used to incomplete information. They are used to trying things before they know if they will work. They are used to roadblocks. They are used to pivoting.
That is the AI environment.
AI is a workshop. It is a lab. It is a trading desk. It is a detective board. It is a poker table. You are constantly testing, updating, filtering, and improving.
People who want exact instructions can still benefit from AI, but the real upside comes from learning how to move through uncertainty. The moment something breaks, the entrepreneurial user studies it. The moment the model gives a strange answer, the entrepreneurial user treats it as information. The moment the path is unclear, the entrepreneurial user starts testing.
That is where the compounding begins.
The strange answer becomes a clue. The roadblock becomes feedback. The bad draft becomes raw material. The failed prompt becomes a new data point.
You do not need the machine to be perfect because your job is to work with the machine.
That is the real mindset shift.
AI rewards judgment.
It rewards the person who can think in probabilities.
It rewards the person who can update without ego.
It rewards the person who can filter signal from noise.
It rewards the person who can keep going when the first answer is not good enough.
The New AI Operating System
The AI age requires a new operating system.
Think in bets, because every prompt is a wager and every output is information.
Update like a Bayesian, because the best path will often reveal itself only after you begin.
Observe like Sherlock Holmes, because the value is in knowing which clues matter.
That is the mindset I am trying to teach.
The goal is not one perfect prompt. The goal is a better way to think.
AI will help you improve the odds.
It will help you see more possibilities. It will help you test more directions. It will help you move faster. It will help you get unstuck. It will help you learn from wrong turns. It will help you build the first version so you can react to it, improve it, and move again.
But you still have to bring the mindset.
You still have to bring curiosity.
You still have to bring judgment.
You still have to bring the willingness to be wrong and keep going.
That is why I believe the great divide in the AI age will be between people who use AI mechanically and people who use AI entrepreneurially.
One group waits for the answer.
The other group improves the odds.
One group stops at the roadblock.
The other group studies the roadblock.
One group wants certainty before beginning.
The other group begins, learns, updates, and keeps moving.
And once you understand that, AI becomes much less intimidating.
It is probabilistic leverage.
The people who thrive will be the ones who know how to use that leverage: thinking in bets, updating as the evidence changes, and observing carefully enough to find the clues everyone else missed. Most importantly, AI is a succession of lessons that must be lived to be understood.

The most important AI story right now is speed.
Every few months, the models get better. Then, suddenly, every few weeks, the models get better. Then one day you look around and realize that the thing many people dismissed as a chatbot has become a coding partner, a research assistant, a financial analyst, a tutor, a strategist, and a personal operating system.
That is what the arrival of Fable 5 represents to me.
The specific model matters less than the direction. The direction is obvious. The frontier is moving faster, the intelligence is becoming more usable, and the gap between the people using AI and the people ignoring AI is widening.
There is a very important example hiding in plain sight. Since February 28, much of the market’s attention has been pulled toward the back-and-forth in the U.S.–Iran war, the risk around the Strait of Hormuz, and the possibility of another oil shock. That focus is understandable. But while everyone was watching geopolitics, the AI model layer kept accelerating. By my count, there have been roughly two dozen notable model releases, model-family launches, previews, or major capability rollouts since then. Anthropic alone shows the speed of the cycle: Claude Opus 4.7, then Opus 4.8, then Mythos Preview, and then Fable 5/Mythos 5. OpenAI released GPT-5.4 and GPT-5.5. DeepSeek released V4 Preview with a 1-million-token context window. Google, xAI, Mistral, Moonshot, MiniMax, Qwen, and NVIDIA all pushed forward as well. The point is simple: AI is not waiting for the world to calm down.
That is the part I do not think most people understand yet.
The risk is no longer that AI disappoints. The risk is that AI keeps improving while many people continue to treat it like a novelty. They try it once, ask it a question, get a generic answer, and decide they understand it. They use it like Google with a personality. Then they move on.
Meanwhile, the models are moving from answering questions to doing work.
That is the divide.
Some people are still asking AI to write a paragraph. Others are asking AI to build tools. Some people are still debating whether AI is overhyped. Others are using it to build personal systems that make them smarter, faster, and more capable.
That second group is beginning to separate.
Last week, I saw something that made me more excited than almost anything I have seen in AI this year.
I uploaded a project for subscribers (ai.22vresearch.com)
showing how to build a Jensen Huang knowledge brain. The idea was simple: take transcripts, organize the source material, connect it to an AI workflow, use an API key, and build something that allows you to ask better questions of a focused body of knowledge.
This was not meant to be a Silicon Valley engineering project.
It was meant to be a bridge for people just like me with no prior coding experience.
The goal was to take people who had mostly used AI as a chatbot and show them that they could build something. They could gather information. They could structure it. They could connect it. They could create a tool that reflected their own curiosity. The could feel empowered.
The response I received in the days after was incredible.
People who had never coded before were building. Adults who had been intimidated by AI were sending me messages saying they had done it. Some of them were proud in the way people are proud when they realize they can do something they thought belonged to another class of person.
Then the next wave came, which was even better.
Those same adults were showing it to their kids.
That is when the whole thing clicked for me.
This was not just a knowledge brain. This was agency.
For the last year, AI has been discussed mostly through fear, market speculation, job loss, regulation, national security, and competition. All of those topics matter. I spend a lot of time on them. The macro implications are real. The investment implications are enormous. The national security implications are arriving faster than most people expected.
Yet at the personal level, the most important question is much simpler.
Do you feel more powerful because of AI, or do you feel more powerless?
That is the entire game.
The people who feel powerless will wait. They will watch the headlines. They will listen to the doomers. They will tell themselves they are too busy, too old, too nontechnical, too late, or too far removed from the center of the action.
The people who feel powerful will build small things.
That is how this starts.
A knowledge brain does not need to begin with Jensen Huang. It can begin with anything you care about. Your favorite investor. Your favorite author. Your company’s internal documents. Your health research. Your industry. Your sales calls. Your podcast library. Your notes. Your family history. Your fantasy football preparation.
That last one is where this gets fun, especially as we approach the beginning of training camps.
As fantasy football season approaches, adults and kids are already starting to talk about building their own fantasy football brains. Imagine taking your favorite podcasts, transcripts, rankings, injury discussions, coaching comments, team previews, and draft strategy shows, then turning that material into your own AI-powered research assistant.
Suddenly, AI is no longer abstract.
It is no longer a scary headline about job replacement. It is no longer a debate about whether the models are conscious. It is no longer just a stock-market argument about capex, chips, power, and margins. It is no longer a way to secure a job. It is fun.
It is a kid asking better draft questions.
It is a parent and child building something together.
It is a fantasy football brain that knows the voices you trust, the analysts you follow, the players you care about, and the strategies you want to test.
That is the on-ramp.
Most people do not become AI users because someone explains artificial general intelligence to them. They become AI users because they build one thing that matters to them. Once they build that one thing, their relationship with AI changes. The intimidation fades. The curiosity rises. The next project becomes easier.
That is exactly why I built my AI subscriber paywall around three ideas: Signal, Alpha, and Agency.
Signal means staying current.
The AI world is moving too fast for people to follow casually. The headlines are noisy. The incentives are messy. The doomers are loud. The hype men are louder. One side wants you terrified. The other side wants you euphoric. Neither is useful by itself.
Signal is about my YouTube channel filtering the noise and understanding the regime. What is actually changing? Which model release matters? Which infrastructure bottleneck matters? Which company comment matters? Which government action matters? Which part of the AI economy is accelerating, and which part is digesting the last wave of investment?
If the models are speeding up, the first job is to stay oriented.
Alpha means turning that signal into investment insight.
AI is not just a technology story. It is a capital cycle. It is an infrastructure buildout. It is an energy story, a chip story, a data center story, a networking story, a software story, a security story, and eventually an application story.
The winners will not be limited to the companies with the most famous chatbots. The buildout is much bigger than that. There will be beneficiaries across the stack, from power to chips to infrastructure to models to applications. The goal of the paywall is to take the signal and translate it into company-level opportunities, because investors need more than excitement. They need a map.
Agency is the third piece, and right now it may be the most important.
Agency means using AI yourself.
It means prompts. Tools. Workflows. Experiments. Buildouts. Examples. Mistakes. Iteration. It means watching someone else do it and realizing you can do it too.
The knowledge brain project was the clearest example yet. It gave people a path from passive user to active builder. It showed them that the API key, the transcripts, the code, and the AI assistant were pieces they could learn to assemble. Once they assembled them, they did not just have a tool. They had proof.
Proof matters.
Proof changes identity.
A person who says “I do not know how to code” becomes a person who says “I built a knowledge brain.” A parent who worries their child is falling behind becomes a parent helping that child build a fantasy football research system. A subscriber who thought AI was something happening to the world begins to see AI as something they can use to shape their own world.
That is the message I want people to hear.
You do not need to become an AI researcher. You do not need to become a professional programmer. You do not need to understand every detail of model architecture, inference scaling, token economics, or frontier benchmarking.
You need to start building.
Start with something you love. Start with something familiar. Start with a small body of information and ask how AI can make it more useful. Build a brain around a person, a topic, a hobby, a market, a sport, a company, or a question you cannot stop thinking about.
The first version can be messy. It should be messy. The goal is momentum.
The models are going to keep improving. Fable 5 will lead to the next model, and the next model will lead to another step change after that. The people who wait for the perfect moment will discover that the perfect moment keeps moving away from them.
The people who start now will compound.
They will learn how to ask better questions. They will learn how to structure information. They will learn how to connect tools. They will learn how to separate good answers from lazy ones. They will learn how to turn AI from a chatbot into a collaborator.
That is why I am so excited.
The knowledge brain response showed me that the audience is ready. Adults are ready. Kids are ready. Investors are ready. Builders are ready. The curiosity is there. The only thing missing for many people is a path.
That is what I want this platform to provide.
Signal to know what matters.
Alpha to understand who benefits.
Agency to make sure you are not just watching the AI revolution from the sidelines.
The world can always give you a reason to wait.
The models will not.
Build the brain before the gap gets wider.

To get things started, I am still tired as I write this. It has been a long couple weeks.
By now, if you have been consuming my content across Substack and YouTube, you probably know I am a Knicks fan. Not a casual fan. Not someone who discovered them when they became relevant again. I mean the kind of fan who carries the team as part of his own emotional history. If you listened to this week’s conversation with Anthony Pompliano, I went into detail about my connection. The Knicks are not just a team I like. My relationship with them as a fan is major part of my development to who I am today. Like the ups and downs in life, I remember where I was during the bad seasons, can recall the false starts, the hopeful trades, the unforgettable plays, the heartbreaks, and the years when optimism felt irrational but somehow still came back every October.
So this past week ending was not just a sports week for me. It was personal.
The Knicks won their first championship in over 50 years, and I was fortunate enough to be invited to Game 4 of the 2026 Finals against the Spurs. I have been to big games before. I have watched historic moments. I have spent a lifetime around markets, pressure, cycles, momentum, and reversals. But what happened inside Madison Square Garden that night was something different.
It was not just a game.
It was a human event.
The first thing I remember about Game 4 was not the final score. It was the sound.
Madison Square Garden did not feel like an arena that night. It felt alive. It felt like one giant nervous system made up of thousands of people who had waited their whole lives for the same thing. You could feel the anxiety before you could hear it. You could feel the history in the building. This was not just a crowd watching basketball. This was a city, a fan base, and generations of memories, many of them dominated by disappointment, all compressed into one room.
The game became historic because it was the largest comeback in NBA Finals history. But that fact alone does not explain why it will stay with everyone who was there. Records are clean. Memories are not. Records turn chaos into one sentence. But the actual experience of living through that comeback was messy, exhausting, emotional, and uncertain.
What made it unforgettable was how it happened.
It was not easy. It was not cinematic in the way sports documentaries make these things feel afterward. There was no single clean moment where everything turned and everyone knew the story had changed. It was a grind back from a beating in the first half. The Knicks had to climb back possession by possession. Every time the Garden started to believe, the Spurs pushed back. The momentum stalled. Hope appeared, disappeared, and then returned louder.
That is what made it feel so much like life. Two steps forward, one step back. Belief tested, then tested again. A little progress, then doubt. A run, then a mistake. A roar, then silence. This was a constant swing from it’s over to they have chance. The human brain wants the clean story after the fact, but the human body remembers the uncertainty in real time.
That is what everyone in that building experienced together. We were not watching a comeback. We were enduring one. And for that night, everyone felt like family.
That is why championships matter so much.
They are not simply about winning. They are about what winning does to memory for a community.
A championship takes every frustrating season for every individual and turns it into part of the community story. It takes the bad teams, the missed chances, the arguments, the jokes, the hope that seemed foolish, the years of waiting, and somehow folds all of it into one shared emotional release. Suddenly the suffering has a purpose. Suddenly the waiting becomes part of the value. Suddenly all those years you thought were wasted become the reason the moment feels so powerful.
That is the strange alchemy of sports.
The pain is not erased. It is redeemed.
For Knicks fans, this championship was not just about the players on the floor in 2026. It was about everyone who came before them. You were reminded of that by the past players in attendance at every game. It was about the versions of ourselves who watched the team when there was no rational reason to keep watching. It was about childhood. It was about parents and children. It was about friends. It was about New York. It was about the Garden.
And ultimately, it was about the shirt.
There is an old idea that fans are not really rooting for the players because the players change. They are rooting for the shirt. On the surface, that sounds cynical. But I think it is actually profound.
The shirt is not just laundry.
The jersey is a vessel.
It carries place. It carries memory. It carries identity. It carries the rituals of being a fan. It carries the games you watched with your father, the conversations you had with your friends, the seasons you complained through, the nights you said you were done and then came back again two days later. It carries a city’s personality. It carries stubbornness. It carries loyalty. It carries disappointment. And when the waiting finally pays off, it carries joy.
That is why Josh Hart’s postgame comment after the Knicks won the championship on Saturday night hit me so hard. He said, “Nobody understands the pressure of wearing that jersey.”
That line summarized the whole thing.
The pressure of wearing that jersey is not just the pressure of a basketball game. It is the pressure of memory. It is the pressure of expectation. It is the pressure of fans who have poured part of their lives into something that, on paper, makes no sense.
Why should a team matter this much?
Why should a logo create this much emotion?
Why should a shirt connect strangers?
Because it is not really about the shirt. It is about what the shirt holds.
That is the part of sports that artificial intelligence cannot replace.
AI is going to create infinite entertainment. It will generate highlights that never happened. It will recreate historic games with different endings. It will make personalized videos, synthetic announcers, deepfake interviews, simulated athletes, and customized storylines. The internet already made media abundant. AI will make it overwhelming. We are moving into a world where the question will not be, “Can content be created?” The answer will almost always be yes.
The question will be: Was it real? Did it happen? Were you there? Did you feel it with other people?
That distinction is going to become one of the most important cultural and economic questions of the AI age.
When anything can be generated, authenticity becomes scarce.
When any image can be faked, proof becomes valuable.
When any performance can be simulated, presence becomes premium.
When entertainment becomes infinite, the real human experience becomes the luxury good.
That is what I felt at Game 4. I was not there because the visual quality was better than television. In some ways, watching at home gives you a better view. You get replays, commentary, angles, statistics, and comfort. But being there gives you something technology cannot replicate. It gives you shared emotion in real time. It gives you the feeling of strangers becoming a community for three hours. It gives you the physical memory of sound moving through your body. It gives you the knowledge that you were inside the moment before history knew what it was going to become. This is why for those who could not attend the game, they went to watch parties.
And the way I happened to be there made the connection even deeper and also symbolic.
I was invited by people associated with Candy Digital, a company built around the intersection of sports, collectibles, digital ownership, and fan identity. That context mattered. I do not want to make this piece about Candy Digital specifically, but sitting there with people who have been thinking deeply about the future of sports collectibles made the entire night feel like a real-world case study. This was not an abstract debate about NFTs. This was the emotional source code of why they may matter.
Because if NFTs have a future in sports, it will not be because people want another speculative image. It will be because people want authenticated memory. They will want proof that they were part of something real. They will want digital objects that connect them to physical experiences, communities, teams, places, and moments that cannot be recreated after the fact.
This playoff run made that even clearer because the story was not confined to Madison Square Garden. Knicks fans were everywhere. After Game 4 on my subway ride home, they packed the subways not from the game but from the bars and watch parties. They traveled. They showed up in opposing arenas. They turned road games into something that felt, at times, like extensions of New York. You could feel it in the chants, in the noise, in the way the orange and blue kept appearing in places where it was not supposed to dominate. Back home, the city started to feel like one giant fan base. Knicks hats, shirts, and jerseys became hard to find because everyone wanted a visible marker of belonging. That was the real story underneath the basketball. People were not just buying merchandise. They were putting on identity. They were saying, “I am part of this.” In that sense, the community was not a side effect of the championship run. It was the center of it.
This is where I think NFTs were misunderstood when they were first acknowledged by the world.
The first wave of NFTs was too focused on speculation and images. People thought of them as digital art, profile pictures, or trading vehicles. Some of that mattered. Much of it did not. But the deeper idea behind NFTs was never only about JPEGs. The deeper idea was about digital ownership, provenance, identity, community, and proof.
Those words matter more now than they did before AI.
Community. Memory. Emotion. Identity. Authenticity. Provenance. Belonging. Proof-of-presence.
That is where sports and NFTs eventually meet.
A Game 4 NFT should not be thought of as a speculative collectible. It should be thought of as a digital memory object. It is the modern version of a ticket stub, except richer, programmable, and connected to a community. It says: I was there. I was in the building when the Garden shook after OG Anunoby’s tip went in. I was part of that comeback. I did not just watch the highlight later. I lived the uncertainty. I felt the fear. I felt the release. I belonged to that moment.
But the more important point is that the NFT does not have to stop with attendance. It can become a community credential. It can say you were part of the run, part of the road takeover, part of the citywide identity shift, part of the emotional network that made the championship feel larger than the team itself. A hat or jersey tells the world what you care about in the physical world. An NFT can do something similar in the digital world, but with memory and proof attached. It can carry the story of where you were, what you experienced, and which community you belonged to when the moment happened.
That has value.
Not because someone can flip it the next day. Not because it has a floor price. Not because it belongs in a hype cycle. It has value because human beings have always collected proof of meaningful experience. We save ticket stubs. We frame jerseys. We keep programs. We take photos. We tell stories. We pass memories down. NFTs are simply the digital evolution of that instinct.
In sports, the best NFTs will not be about replacing the physical world. They will be about connecting the physical and digital worlds. They can authenticate attendance. They can unlock communities. They can connect fans who shared the same moment. They can become badges of loyalty. They can attach to video, stats, memorabilia, access, fantasy games, or future experiences. They can turn a fan’s personal history into a verified digital identity.
That matters because the future of fandom will not only be about consuming content. It will be about belonging to something.
In an AI world, live sports may become even more important, not less. Sports are one of the few remaining forms of mass culture where the outcome is unknown, the emotion is live, and the community is real. A championship game is not generated content. It is a collective human experience. It cannot be recreated afterward in a way that equals the original, because the uncertainty is part of the experience. The fear is part of it. The doubt is part of it. The waiting is part of it.
The more AI floods the world with synthetic media, the more valuable it will be to prove that something was real. The more entertainment becomes personalized and artificial, the more people will crave shared experiences that cannot be individually generated. The more deepfakes blur the line between truth and fiction, the more important authenticated memory becomes.
That is why the Knicks championship meant so much to me. It was not just the end of a drought. It was a reminder of what technology cannot manufacture.
AI can create a perfect image of a Knicks celebration. It can write a fake recap. It can generate a video of a crowd roaring. It can simulate the sound of Madison Square Garden. But it cannot give you the feeling of standing there as belief slowly returned to the building. It cannot reproduce the exact emotional weight of waiting decades for something and then realizing, in real time, that it might actually happen. It cannot manufacture the life you lived before the moment arrived.
That is the human premium.
The future may be increasingly digital, but the most valuable digital assets will be the ones that point back to something deeply human. Not synthetic status. Not artificial scarcity. Real memory. Real presence. Real community. Real emotion.
Game 4 reminded me that the most powerful experiences are not consumed alone. They are shared. They move through a crowd. They attach themselves to place. They become part of who we are.
That is why I believe NFTs still have a future in sports. Not as hype. Not as speculation. Not as technology looking for a use case. But as authenticated containers for the things fans already care about most: memory, identity, community, emotion, and proof that we were part of something bigger than ourselves.
The Knicks won a championship. I was there for one of the games that helped define it. And years from now, the box score will still tell people what happened.
But it will never tell them what it felt like.
That is why the experience matters.
That is why the memory matters.
And that is why, in an age of artificial intelligence, the most valuable thing may not be what can be generated.
It may be what can be proven to have been lived and I have lived with the Knicks!

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(truncated — read full post on source)

The Singularity has stopped having a ceiling and started having a horizon. OpenAI’s Noam Brown notes the plateau “is actually really far out these days,” [ https://substack.com/redirect/5b7f0596-ee4f-4338-8eb6-c8bb5270890d?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] with a well-scaffolded GPT-5.5 able to think for weeks before benchmarks flatten. Nous Research turned that depth into product, exposing mixture-of-agent presets as virtual models [ https://substack.com/redirect/61fbf825-69c8-4890-b974-95c3d74c922e?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] that claim to beat Opus 4.8 by 8% and GPT-5.5 by 11% on their own upcoming benchmark. The same frontier is becoming a commodity, fragmenting downward in price, as DeepSeek’s MIT-licensed V4-Pro-DSpark [ https://substack.com/redirect/43b6cae0-50ec-45a9-a918-abbb65531a16?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] folds 1.6 trillion parameters and a 1M context into speculative decoding that sips a tenth the KV cache, and a field guide to open weights [ https://substack.com/redirect/b428a4ef-1517-4918-9ea8-78f9733804b4?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] crowns DeepSeek, GLM 5.2, MiniMax M3, and Nemotron 3 Ultra as frontier-class coders holding the same three-to-six-month gap they have kept for eighteen months, now at a sliver of the cost. Up top, Shopify’s CTO finds GPT-5.6 beats Opus at everything yet still cedes coding to Fable 5, [ https://substack.com/redirect/20eac5fe-8a12-41b8-ba69-d83eb5a20a02?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] pairing them so Fable writes code while 5.6 runs experiments, and Elon Musk says Grok 4.5 is already nipping at Opus [ https://substack.com/redirect/93717b92-f998-4e05-95bb-141e6e270944?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] inside SpaceX and Tesla.
Capability this sharp cuts both ways. Anthropic looks set to restore Fable 5 within the week, [ https://substack.com/redirect/8807c37a-20e1-4c6c-82d3-78fa56f0531f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] days after Mythos 5 returned for trusted users, the pause itself a backhanded tribute to real cyber teeth. Those teeth are no longer uniquely American, with Chinese systems reportedly matching Mythos in cybersecurity, [ https://substack.com/redirect/f50aa0d2-cf8c-48ec-91d7-e57d9fa6c2cc?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] and as the export ban drags on, China’s 360 ships Tulongfeng and Tokyo’s Sakana ships Fugu [ https://substack.com/redirect/4a25c4f0-fe9e-4b78-8cc4-0fb28dc24901?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to fill the Mythos-shaped hole. One analyst warns the real fallout is China waking up, [ https://substack.com/redirect/0d5e7db0-0860-4cc3-b94b-b3bb26101b10?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] reasoning that a CCP on the receiving end of NSA-embedded offensive operations will stop believing it is four months behind and start sprinting, even as the NSA’s own cyber benchmark, due by early August, looks like the gate that finally clears both Fable 5 and GPT-5.6 for the public.
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The models that can bankrupt you can also budget for you. Coinbase’s Brian Armstrong nearly halved AI spend while usage soared, [ https://substack.com/redirect/b4f31b19-d238-4762-ac08-e79000529adb?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] using cheaper defaults, smarter routing, and warm caches rather than usage caps, in one case dragging a cache hit rate from 5% to 60%. Perplexity’s CEO sees every enterprise spinning its own model-harness-sandbox-eval flywheel [ https://substack.com/redirect/1b732d1b-9969-4b8d-add6-628dea4a3226?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] tuned for token value per watt, while engineers lean into “loop engineering” [ https://substack.com/redirect/641b01f2-daab-425b-a4e9-cb668532015f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] so completely that Claude Code’s creator no longer writes his own prompts, Claude does. Anthropic’s Claude Tag, an AI teammate inside Slack channels, has left staff at Slack owner Salesforce confused even as the company promotes it. [ https://substack.com/redirect/6833da67-e16a-48ff-9bc7-e168c72a73db?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] The bill still looms, as Gartner expects AI coding costs to outrun the average developer’s salary by 2028, [ https://substack.com/redirect/ec9721f7-e91b-4c31-9ea3-59a1f193dc91?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] which helps explain founders running half a dozen agents at once who say they have never worked harder while shipping 100x more and burning out anyway. [ https://substack.com/redirect/08520784-552e-4767-b8e6-7c2ec4adf42b?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ]
All of it runs on a physical plant scrambling to keep pace. IBM is industrializing quantum, [ https://substack.com/redirect/da1fcdd0-6f73-4687-b028-0410837d6066?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] seeding its Anderon foundry with $2 billion, half from Washington, and pledging $9 billion more over five years toward its 2029 Starling machine. Compute is so scarce that Google throttled Meta’s Gemini access [ https://substack.com/redirect/fcd0bad4-fcf9-45eb-aa18-f6e2fb49c409?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] after Meta asked for more than it could spare, and Masayoshi Son is betting against Musk’s orbital data centers, [ https://substack.com/redirect/f7a61198-3d16-48a0-b4d2-411c5497e0a9?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] asking “What’s the point?” when power is barely 7% of operating cost and orbit is a decade too slow. Power is going exotic and abundant anyway, as General Fusion tripled its plasma to 8.4 million degrees by mechanical squeeze alone, [ https://substack.com/redirect/e8f6bf19-2c46-4b94-95b2-3827f2d4d4f2?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] renewables reached 30% of US generation with solar overtaking wind, [ https://substack.com/redirect/d33242b9-e256-43ba-804b-4ace9fee5026?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] and SpaceX began laying an eight-mile “Starpipe” [ https://substack.com/redirect/e1ca678b-723c-46e8-9579-68103bd0f581?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to pump methane straight to Starship.
Atoms are catching up to bits. A $300 wristband called ForceBand [ https://substack.com/redirect/0449300b-dca0-4017-8965-be1b17538941?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] teaches robots force-aware hands from human muscle signals, hitting 87% on pick, squeeze, and place, while AGIBOT rolled out its 15,000th robot [ https://substack.com/redirect/503be676-8bd9-4232-9609-b5d610b137df?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] already holding a 39% share of humanoid shipments. The machines are taking the field too, as Taiwan budgeted $6.6 billion for an “unmanned shield” of 208,200 one-way attack drones, [ https://substack.com/redirect/ed756ab5-5299-4fc2-983a-9a20a2de2621?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] and in a national first a Sacramento sheriff’s drone used a magnet to lift a knife from a suspect’s hand [ https://substack.com/redirect/08d26642-5f5a-4a23-b297-aa5e8c622db8?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] before deputies moved in.
Discovery itself is automating, inward and outward. Erdős problem #870 fell to GPT-5.5-Pro, [ https://substack.com/redirect/2e0b5ccb-d8cb-4119-801d-b215ba991bbb?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] then held up across 180,000 sorry-free lines of Lean 4, a scale no one had formalized before. A lone PhD synthesized PAC-832, the first selective GalR1 antagonist for Alzheimer’s, in a garage lab [ https://substack.com/redirect/55c538fb-c9f0-4924-985b-7256d18e9dc9?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] run by Claude Code and a liquid-handling robot. Clinicians in Haifa used focused ultrasound to drop an opioid patient’s cravings to zero in 20 minutes, [ https://substack.com/redirect/c93c86c6-a1b4-4660-ada5-085223bbfa17?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] and SETI’s Andrew Siemion proposes sifting one cubic meter of lunar regolith [ https://substack.com/redirect/eac4f759-cfc0-4894-86cb-18fec7b78cd1?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] for micron-scale alien debris, the nearest place to look for proof we were never alone.
The proof is in the powder.
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Over the years, people have had a changing relationship with protein. There is always a new take. More protein. Less protein. Animal protein. Plant protein. Protein for muscle. Protein for weight loss. Protein for longevity.
Here is my simple guideline for my HRV journey based on my own dot connections from my extensive rabbit hole protein journey: protein is one of life’s essential building blocks. Stop there for a second and just think about if you know how much protein you need a day as you get older. Then think if you know if animal protein and plant protein can be used by your body the same. Once you do that simple work recognize protein gives the body the raw materials to repair tissue, preserve muscle, support immunity, produce key enzymes and hormones, and maintain the resilience needed to recover, adapt, and age well. If there is one thing you understand for aging, you must understand protein.
Most people still think about protein through the lens of muscle. They think about lifting weights, building strength, or avoiding the frailty that comes with age. That is all true. But reread that line above and the bigger point becomes clear: protein is not just about muscle. It is about the quality of your recovery, your ability to adapt, and the way your body ages.
And if you care about heart rate variability, that combination is the entire game.
HRV is a signal from beneath the surface, showing how well your nervous system is managing stress, recovery, and adaptation. It comes from the autonomic nervous system, the part of the body that constantly adjusts heart rate, breathing, blood pressure, digestion, inflammation, and recovery. When HRV is higher, especially the RMSSD number commonly used by wearables, it usually means the parasympathetic nervous system has more influence. That is the branch associated with rest, repair, digestion, and adaptation. When HRV is lower, it often means the body is carrying more stress than it can comfortably process.
That stress can come from anywhere. A hard workout. A bad night of sleep. Alcohol. Emotional strain. Illness. Inflammation. Too much food too late. Not enough food. Or simply the accumulated wear and tear of living in a high-output state.
This is where protein enters the HRV conversation.
Read more

Aside from all the specific changes I have made in my life to raise my HRV, none of this journey would have been possible without first learning what HRV actually is. That may sound obvious, but it was not obvious to me at the beginning. Like most people, I first saw HRV as a number. A recovery score. A daily grade. Something to improve.
Over time, I realized that was too narrow.
One of the reasons I keep encouraging readers to take what I write and put it into an LLM is because HRV is deeply personal. The number becomes more useful when you connect it to your own life: your sleep, your stress, your training, your alcohol, your age, your work, your family, your mind, and your patterns. The power of AI in this context is its ability to help you ask better questions about your own system.
Most people who reach out to me about this Substack focus first on heart health. That makes sense because HRV is measured through the heart. But by now, you know I do not think of HRV as simply a cardio fitness score. Overall health matters, of course, but many of the people who struggle with HRV are Type A, high-performing people who are already in excellent cardiovascular shape. They exercise. They work hard. They are disciplined. And yet they are frustrated because their HRV is lower than they expect.
That is where the real lesson begins.
HRV is telling you about the system behind the heart. It is a signal of regulation, recovery, stress, and adaptability. It is one of the ways the body reveals whether it has capacity or whether it is quietly carrying too much load.
That is why I now connect HRV to anti-aging. I did not start this journey by trying to “anti-age.” I started because I wanted to understand why my body was giving me a signal I did not fully understand. But the deeper I went, the more I came to see HRV as part of the relationship between my biological brain and my computational brain. My body was producing data. My devices were capturing it. AI was helping me interpret it. Over time, the combination helped me understand myself in a way I could not have done before.
That is the larger point of this paper. I do not want to approach HRV only through my own success in raising it. I want to come at it from another angle: its history. Because once you understand where HRV came from, especially its role in space medicine and human adaptation, the metric starts to look very different.
HRV is a signal to understand.
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This week I recorded a video about how to get started with AI. It was inspired by the question I have been asked more than any other over the past year: “Where do I start?” As I was finishing the video, it struck me that I receive almost the exact same question from readers following my HRV journey. They want to know the one thing they should focus on if their goal is to improve their health, raise their HRV, and slow the aging process. To combine these two critical areas of learning, at the end of this post, I have included a prompt you can use to continue your own learning journey around breath and HRV. It is designed to act like a personal breathing, nervous system, and HRV coach, teaching you one concept at a time, giving you daily exercises, asking you to reflect on what you feel, and gradually helping you understand how breath, the vagus nerve, stress, recovery, and longevity are all connected. The process of incorporating lifestyle changes for longevity and anti-aging must begin with learning.
As I have written many times, my research says there is no single solution to anti-aging or improving HRV. The human body is an incredibly complex system. Every intervention comes with tradeoffs. Changes that produce benefits in one area can often create unintended consequences somewhere else. Sleep, nutrition, exercise, recovery, relationships, stress management, and purpose all matter. They interact with one another in ways that are often difficult to fully understand.
Yet among everything I have experimented with, one habit stands above the rest. It is my answer to the question. It happens to be the first letter in my HRV framework, MINES: Mindful breathing. Learning how to breathe properly and becoming aware of my breath has produced profound changes in my life. The improvement showed up in my HRV data, but the impact extends far beyond a number on a wearable device. It has changed how I handle stress, how I build my business, my relationships with family and friends, my relationship with food and alcohol, my approach to exercise and sleep, and even how I think about the future.
One lesson continues to become clearer with time. Just as the human body is a complex system, so are our lives. We often underestimate the importance of the environments we create and the relationships we cultivate. It is one reason I spend so much time in Maine. It is why I chose to learn skiing in my forties while walking away from golf and basketball. As I get older, I increasingly view life through a simple lens: we get one chance to live it. I want to be healthy, happy, present, and able to enjoy every minute of it. For me, awareness of my breath has become the foundation for pursuing that goal. Without breath, there is no life. With mindful breath, there is greater calm, resilience, and joy in the life we have.
Understanding why breath has such a powerful effect requires a basic understanding of the vagus nerve which I have written about in prior posts. Acting as the primary communication highway between the brain and the body, it carries signals among the heart, lungs, digestive system, and nervous system. Through that network, it helps regulate the balance between the sympathetic nervous system, which drives the fight-or-flight response, and the parasympathetic nervous system, which supports recovery, healing, and resilience. Fight-or-flight is not just about running from danger. It often shows up in everyday life as anxiety, anger, road rage, emotional reactions, poor sleep, overeating, excessive drinking, and the feeling that you are constantly rushing from one thing to the next without any awareness. Leah Lagos’s book,
Heart, Breath, Mind
, was instrumental in helping me understand this relationship through breaking down the science of HRV. Her work helped me see breathing not as a relaxation exercise but as a tool for training the nervous system itself. If you have heard me recommend the Art of Learning by Josh Waitzkin in my work, I found Leah’s work through Josh. This is again an example of my healthy relationship with the environment I create. In this case, with the people I follow for inspiration.
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