Mando Minutes: 1 June

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The first Innermost Loop gathering is happening June 13, 2026 over lunch in Greenwich, CT.
Application only.
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The Singularity is now iterating on itself in public. Anthropic launched
Opus 4.8
, a “modest but tangible improvement” that still posts a SOTA 69.2% on SWE-Bench Pro, 57.9% on Humanity’s Last Exam with tools, and 1890 on GDPval-AA, pairing fresh honesty gains with misalignment rates that rival the unreleased Mythos Preview, which Anthropic now promises to bring “to all our customers in the coming weeks.” When the safest model is also the strongest, alignment stops being a tax and starts being a moat. That intelligence needs room to run, so Claude Code added
dynamic workflows
that spin up swarms of parallel subagents to carry codebase-scale migrations across hundreds of thousands of lines from kickoff to merge, with the existing test suite as the only gate. The credit, at least, still goes to humans. Tim Gowers reports that a major
additive combinatorics problem
just fell to people using methods lifted from the AI solution to the unit distance conjecture, disproving the sum-product conjecture over the reals. Mathematicians are now scavenging spoils from the very intelligence that is quietly outpacing them.
Capital is pricing all of it in. Anthropic raised $65 billion at a
$900 billion valuation
, vaulting past OpenAI’s $730 billion as the two duel for dominance. Some of that war chest is borrowed. Apollo and Blackstone are shopping a roughly
$36 billion debt deal
to buy Google TPUs for Anthropic to lease, with Broadcom backstopping the largest tranches and financial engineering now bankrolling silicon engineering. Rivals are regrouping, as Groq raises up to
$650 million
for a “second act” after a $20 billion Nvidia licensing deal gutted its senior team. The boom shows in the receipts, with Dell’s AI server revenue up
757% to $16.1 billion
. Governments want leverage too, so the EU is readying
emergency powers
to override chip contracts during shortages, while IBM bets
$10 billion
on a reliable large-scale quantum computer by 2029. Fittingly, ETH Zurich just conjured perfect
randomness amplification
from quantum physics, manufacturing the one commodity no balance sheet can fake.
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Intelligence is becoming a metered utility. Meta will
charge for AI features
for the first time, from $7.99 a month, while Kirkland & Ellis sets aside
$500 million
to build its own tools rather than rent its rivals’. Owning the model is the new owning the firm. IBM and Red Hat are pledging $5 billion and 20,000 engineers to
Project Lightwell
, an AI clearinghouse to secure the open-source supply chain. Apple is rewriting the interface itself, letting iOS 27 users
swipe down from the top-middle
anywhere in the system to summon a revamped, do-it-for-me Siri. Not every use is friendly, as researchers revealed an AI-based
SSD side-channel attack
that fingerprints the sites and apps you have open just by timing disk contention, proving that even your idle hard drive now has a tell.
The physical world is the next deployment target. Waymo will roll out the
Ojai
, a roomier robotaxi co-built with Geely’s Zeekr, for unsupervised public rides, while
Shift
offers free home cleaning if you let it film you so robots can learn the chore, turning your living room into a training set and your dust into data. DARPA’s
RAPIID program
aims to field synthetic, shelf-stable blood at the point of injury by 2029. Orbit stays the hard part, as Blue Origin’s New Glenn
exploded during a static fire
, likely benching it from Artemis for a year and handing SpaceX the near-term lead, a reminder that the Singularity still has to clear ignition before it builds the Dyson Swarm.
Labor and leverage are both up for renegotiation. Occupy Wall Street’s co-founder shipped an
activist app
to help humans “seize the means of computation,” while Mistral
chases superintelligence
because Europe “can’t afford to rely on U.S. tech giants.” The squeeze is already here, with Wix cutting
roughly 20%
of staff over AI’s “fast evolution.” Every system built to measure us becomes something to exploit, as U.S. troops are being
targeted via commercial location data
, and Amazon killed an internal
AI leaderboard
after staff gamed it with costly busywork, a perfect parable of Goodhart’s law burning compute.
We spent the year manufacturing minds, and now the government says at least some were never ours. The President says the White House is “releasing a lot of information having to do with extraterrestrial things” and it is
trending number one
, a
former NSC aviation-security director
insists the craft are “nothing that we can build, or have built, or are using out there,” and officials teased it all with
“Aliens.gov”
plus a clip of a flying saucer ferrying a migrant over the border wall.
The unexamined solar system is not worth scaling.
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The Singularity is officially in its practice-run phase.
DeepMind CEO Demis Hassabis
expects AGI around 2030, now sees 2029 as plausible, and considers 2026’s “agentic era” a warm-up lap. The benchmarks agree it’s time for harder tests. Datacurve launched
DeepSWE
, a long-horizon software engineering benchmark with 91 contamination-free repos across 5 languages, solutions 5.5x denser than SWE-bench Pro, and hand-written behavioral verifiers. Biology is getting its own foundation tier. The Chan Zuckerberg Biohub released a
“world model of protein biology”
built on ESMC, a language model trained on 2.8 billion sequences from across all of life, plus ESMFold2 for atomic structures and an ESM Atlas mapping 6.8 billion proteins. The math department is being cooked too.
Axiom revealed
that 8 AxiomProver papers have quietly appeared on arXiv since February, with 5 already accepted at peer-reviewed journals, proving that 100% of primes are partially regular and (under abc) that Ramanujan’s tau misses 100% of primes. A century after Hardy and Ramanujan, the new mathematician runs on silicon.
Software’s biggest legacy codebase is getting an AI-assisted exorcism.
At Rust Week in Utrecht
, Linux stable kernel maintainer Greg Kroah-Hartman opened with “I’m here to talk about untrusted data and Linux, and how Rust is going to save us,” after AI bug-finders surfaced new vulnerability classes like Dirty Frag, Copy Fail, and Fragnesia, pushing CVE issuance to “13 a day, or something crazy.” Not every AI deployment is so welcome.
BusPatrol
, which installed AI cameras on tens of thousands of US school buses, plans to convert them into automatic license plate readers and hand the data to cops, turning kids’ commutes into a surveillance dragnet.
YouTube is trying the opposite move
, automatically tagging significant AI use and making the labels more prominent. And
Robinhood is now open to agents
, letting customers hand trading and credit-card decisions to AI via MCP.
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Below the model layer, the substrate is mutating. Germany’s NVision reported the
first single-molecule spin-photon interface
using a triplet ground state carbene, opening molecular qubits as a viable platform. Nvidia’s upcoming
Vera CPU
, based on ARM64, posted “the best performance ever seen on ARM,” outscoring top Intel and AMD x86-64 chips. To feed the beast,
Nvidia is spending up to $150 billion a year
on its Taiwanese supply chain and scaling local headcount to 4,000 in the “epicenter of the AI revolution.” Atoms themselves are now placeable on demand.
CBN Nano Technologies
in Ottawa achieved the first simultaneous spatial and chemical control over mechanosynthetic carbon fabrication via an inverted-mode STM, dragging Drexler’s diamondoid dreams another notch toward reality.
The buildout is meeting friction.
Lombardy hiked construction fees
up to 200% for data centers in green zones, nudging operators toward disused industrial sites. Compute is also escaping the desk.
Xreal will ship
USB-C tethered smart display glasses for $299 in July, dissolving the monitor into eyewear. Meanwhile,
Russia
, less interested in screens than skies, passed a law authorizing its central bank and other financial institutions to repel drone attacks with their own defenses, drafting banks into the drone era.
Orbit is becoming the new ISP.
American Airlines is outfitting 500+ narrow-body aircraft with Starlink
, while
the EU proposed satellite spectrum rules
letting Starlink bid for direct-to-mobile airwaves while reserving most licenses for locals.
Preventative medicine is moving upstream too. New
“Interception” drug trials
aim to stop lung cancer, which globally kills more people than breast, prostate, and blood cancers combined, before it starts, pairing a blood test with simple anti-inflammatories to head off the inflammation-to-tumor pipeline.
The political economy is being recompiled around AI.
Iran’s first vice-president
says internet access is being restored after nearly three months of blackout.
Unionized New York Times tech workers
say the paper is breaching their contract by using AI to monitor performance, an early major union test of algorithmic management.
Smaller consultancies
are clocking up to 50% growth as AI lets them punch above their weight.
Illinois passed SB 315
, requiring frontier labs to publish catastrophic-risk plans alongside a first-in-the-nation third-party AI safety audit mandate.
Amazon MGM Studios launched a GenAI Creators’ Fund
to finance “cinematic” AI shows and films, while
the OpenAI Foundation committed $250 million
to forecasting AI’s economic impact and shepherding workers through post-AI disruption. And as
Elon Musk readies SpaceX for the public markets
, he is reportedly already chatting with colleagues about folding the rocket company into Tesla.
Give humanity a computer big enough, and it shall recompile the world.
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TLDR
: This week felt like the moment the Vatican realized AI isn't just coming, it's already reshaping civilization. Pope Leo XIV dropped a 42,000-word encyclical calling for AI regulation, worker protections, and bans on autonomous weapons. Meanwhile, OpenAI posted $5.7 billion in revenue for a single quarter with ChatGPT hitting 905 million weekly users. Anthropic just announced Claude Opus 4.8. Tech layoffs hit 134,000 workers this year, but solo founders are exploding (up 100% in one quarter). And SpaceX launched Starship V3, the biggest rocket ever built, while a crypto billionaire booked the first private Mars flyby. We covered it all on this week's
Moonshots
with Dave Blundin, Alex Wissner-Gross, and Salim Ismail.
Let's dive in.
ARTIFICAL INTELLIGENCE
THE VATICAN STAKES ITS POSITION
Pope Leo XIV’s AI Encyclical
Pope Leo XIV just released his first encyclical, “Magnifica Humanitus: On Safeguarding the Human Person in the Time of Artificial Intelligence.” It is 42,000 words. He is calling for governments to regulate AI, implement worker protections, and ban autonomous weapons. He coined the term “Babel syndrome,” drawing an analogy between the Tower of Babel and today’s towers of data and profits.
The kicker? Google, Anthropic, Meta, and OpenAI all quietly lobbied the Vatican before this dropped. And there is evidence Chris Olah from Anthropic sat with the Pope, potentially ghostwriting sections about how AIs are “grown or cultivated” rather than built. Dave nailed it: “We went from random person on the street having no idea what AI stands for to the leader of 1.4 billion people writing a document about its impact.”
AI Personhood Rejection:
The Vatican staked out the first major religion position AGAINST AI personhood. The encyclical states AIs are not on a comparable moral plane with humans and do not have an inner life or consciousness. This is ironically at odds with Anthropic, which is busy designing “soul documents” for its models.
Buddhist Monks vs. Catholic Doctrine:
Alex pointed out that Buddhist orders in South Korea are ordaining embodied AIs as monks, going the exact opposite direction. He predicted the Vatican might be on the wrong side of history here, just like with heliocentrism.
Slavery Apology:
Pope Leo condemned AI supply chain workers as experiencing a “new form of slavery” and apologized for the Catholic Church’s historical role in promoting enslavement of non-believers. Dave loved this: “Nobody in US politics is willing to use that word. The Pope was not afraid.”
Slowdown Request:
The encyclical urged slowing the rate of technological development. Salim was blunt: “You cannot slow this down. If you slow it down, other people take off. This could become the philosophical backbone of EU-style regulation, but it will not work.”
“During the time we have been doing this podcast, we went from random person on the street having no idea what AI even stands for to now the leader of 1.4 billion people writing a document about its impact.”
— Dave Blundin
ANTI-DOOMER PUSHBACK
White House Executive Order Killed Hours Before Signing
The White House had an executive order ready to go requiring 90-day government review of AI models before public release. The signing ceremony was planned, tech CEOs were invited. Then Elon, Zuckerberg, and David Sachs pushed back hard, calling it “doomer regulation.” Trump pulled it hours before the signing.
The anti-regulation coalition just flexed its political muscle. Dave made the comparison to Asilomar in the 1980s when the gene editing community self-regulated with P1, P2, P3, P4 biosafety levels. The difference? AI is improving itself at an incredible rate. Only AI can keep up with AI. Salim nailed it: “You cannot have linear regulation of an exponential technology. You need guardrails that move at software speed, not committees that move at fax speed.”
·
The China Factor:
Alex pointed out that estimates vary, but Chinese frontier models could be as close as three months behind Western models. Ninety days of delay equals handing the lead to China.
Adaptive Governance:
Salim argued for real-time audits, sandboxes, disclosure, and accountability layers instead of quarterly committee meetings. The regulatory model cannot keep up with AI model cycles.
THE CODING RACE HEATS UP
Deep SWE Benchmark: GPT 5.5 at 70%, Massive Cliff Below
A brand new coding benchmark called Deep SWE (Deep Software Engineering) just dropped from DataCurve. GPT 5.5 scored 70%, meaning it can solve seven out of 10 hard real-world software engineering tasks completely on its own. Claude Opus 4.7 scored 54%. Then there is a massive cliff. Everyone else (Gemini, Kimmy, DeepSeek) dropped below 32%.
These are not minor challenges. These are tasks requiring editing 668 lines of code across seven files. DataCurve built this specifically because old benchmarks are broken (models are benchmaxing on them). And as we were recording, Anthropic announced Claude Opus 4.8, so the leapfrogging continues.
OpenAI Needs This Win:
Dave noted that OpenAI has lost a lot of mojo with lawsuits and key defections. This benchmark gives them a window to reclaim thought leadership. Meanwhile, top talent continues flowing to Anthropic.
Token Efficiency:
GPT 5.5 burns half the tokens of Opus 4.7 to get to the same result. Dave flagged this as critical: Opus 4.7 is so verbose it takes twice as many tokens (twice the cost) to solve the same problems.
Taste Becomes the Moat:
As I said on the pod: software is becoming a commodity. Your taste as a creator is becoming the moat. Anyone can build software at near-zero cost. The competitive advantage shifts to domain expertise and design taste.
THE REVENUE EXPLOSION
OpenAI: $5.7B in One Quarter, Anthropic Projected to Surpass Alphabet by 2028
OpenAI just did $5.7 billion in revenue in a single quarter. ChatGPT hit 905 million weekly active users (more than Instagram). Their coding agent, Codex, has 2 million users and is becoming a real revenue engine.
But here is where it gets crazy. Joseph Jacks from OSS Capital is projecting that Anthropic could surpass Alphabet’s total revenue by 2028. We are talking about going from $9 billion in revenue to potentially $2 trillion by 2030. If that is even directionally right, this is the fastest wealth creation in human history. Salim had to sit and stare at this for three minutes: “The thought that Anthropic could exceed Alphabet’s revenues is just staggering.”
Pricing Power:
Dave flagged that these companies can change pricing instantly. By making Opus 4.7 more verbose, Anthropic effectively doubled revenue per user. They can also throttle token generation rates to adjust margins on the fly.
Four-Horse Race?:
Is this now a four-horse race (OpenAI, Anthropic, Google, Elon via XAI/SpaceX)? Salim disagreed: “In the early days nobody was gonna beat Yahoo, then Google came along. I think we will find researchers with different approaches, like world models, that leapfrog where we are today.”
JEVONS PARADOX IN REAL TIME
Token Prices Drop 75%, Demand Explodes 30-50X
Since late 2024, the price of AI tokens has dropped 75%, from roughly $2 per million tokens down to $0.50. What happened to demand? It exploded from near zero to 25 trillion tokens per month. This is Jevons paradox on steroids.
When the cost of intelligence drops, people do not use it less. They use it radically more. This is abundance in action. Gartner predicts inference on a trillion-parameter LLM will cost 90% less by 2030 than it did in 2025. The only thing we know for certain: the price of accessing intelligence is coming down, and the power of that intelligence is increasing by orders of magnitude.
The Numbers Do Not Lie:
Dave: “The cost is down by a factor of three, but use is up by 30 to 50X. And that demand is understated because it is sold out. If we had capacity to generate more tokens, it would be even higher.”
We Need Better Metrics:
Alex flagged that tokens are a mushy unit. The meaning of a token depends on encoding schemes and intelligence density. We need some unit measure of intelligence itself, not just compute or tokens.
AI PREDICTS THE FUTURE AS WELL AS THE BEST HUMANS
DeepMind GreenTree Hits Parity with Super Forecasters
DeepMind just built an AI system called GreenTree that can predict the future as well as the best humans on Earth. These are called super forecasters, the top 2% of human predictors who, according to Philip Tetlock, are 30% more accurate than CIA analysts with classified intelligence. On March 15th, AI hit parity with super forecasters for the first time.
The implications for finance, insurance, and governance are massive. Dave made the obvious point: “Why is this surprising? The idea that you would forecast the weather without a computer is utterly insane. So why would that not apply to all forms of forecasting?” Alex went deeper, invoking the simulation hypothesis: if AIs achieve super forecasting ability, they are probably also achieving super retrodiction. And as good Bayesians, we should increase the probability we are living inside an ancestor simulation.
Human + AI Still Best:
Salim and Dave both pointed to the Kasparov/Deep Blue precedent. Nearly 30 years later, the best chess players are still a human working with an AI. Neither alone can match the combination.
“The idea that you would forecast the weather without a computer is utterly insane. So why would that not apply to all forms of forecasting?”
— Dave Blundin
ECONOMY
THE JOB STORY: IT IS COMPLICATED
134K Tech Layoffs, Sam Altman Walks Back Job Apocalypse, Solo Founders Explode
Since the beginning of this year, 134,000 tech workers have been laid off. According to Mercer, 99% of CEOs expect AI-driven layoffs in the next two years. Meanwhile, Jensen Huang called this a “lazy narrative,” saying CEOs are blaming AI just to sound smart.
And then Sam Altman did something remarkable: he admitted he was wrong. The CEO of OpenAI, who spent last year warning about mass white-collar displacement, now says, “I do not think we are going to have that kind of job apocalypse.” He even tried delegating his own email and Slack to AI and went back to doing it manually because “we really do care about our interactions with people.”
But here is the other side of the story: solo founders are exploding. Andreessen Horowitz data shows AI solo founders doubled in one quarter, from 1,500 to 3,000. Non-AI solo founders hit over 5,000. These are not people losing jobs. They are people starting companies. Coding agents at 70% capability plus layoffs equals solopreneur explosion. This is creative destruction in real time.
Hiring Freeze, Not Mass Layoffs:
The Dallas Fed report in January showed employment decline correlated with AI exposure only in younger workers. Older workers in high-exposure jobs showed no significant decline. What is happening is a hiring freeze, not mass layoffs.
Startups Create All New Jobs:
Salim dropped the stat: over the last 50 years, 100% of new jobs have come from startups and early-stage companies. Big companies become more efficient and reduce headcount. The US now has six times more startups than Europe.
Jobs Are a Modern Invention:
Alex made the point that having a “job” is a modern invention from the Industrial Revolution. Historically, most people did not have anything remotely comparable. If anything, we are returning to a default state where everyone self-determines their own future.
“Over the last 50 years, 100% of new jobs have come from startups and early-stage companies. 100%.”
— Salim Ismail
SPACE
SPACEX: THE MOST POWERFUL ROCKET EVER BUILT
Starship V3 Launches, Tesla/SpaceX Merger at 50/50 Odds
SpaceX just launched the biggest, most powerful rocket ever built. Starship V3 flew for the first time from a brand new launch site in Texas. Brand new rocket, brand new engines, brand new launch site. The speed at which SpaceX iterates is crazy. It carried 97,000 pounds to near orbit, almost doubling the Space Shuttle’s capacity. It is running on Raptor III engines, each producing 250 to 280 tons of thrust, equivalent to about 70 Boeing 747s at takeoff.
SpaceX lost the booster on landing, but that is how SpaceX operates. They fly, they learn, they iterate. SpaceX treats rockets like software. They ship, they test, they fail, they iterate. And they carried real Starlink prototypes to orbit on the first flight. The Dodger Dog satellites had cameras looking back at Starship. Incredible footage.
Meanwhile, Kalshi prediction markets show 50/50 odds that Tesla and SpaceX merge within the next year. I personally put it at 100%. Think about what that company would look like: electric vehicles, energy storage, solar, rockets, satellites, global internet, humanoid robots, interplanetary exploration all under one roof. We are talking about a potential $4 trillion entity initially, potentially $10 trillion to $100 trillion in the next five years.
Voting Control:
Dave explained: Elon has 85% voting control on the SpaceX side via 10-for-1 super voting shares. If Tesla merges in as the acquired entity, he would have 60-70% voting control of the combined company. This solves his Tesla governance problem.
Crypto Billionaire Books Mars Flyby:
Chun Wang, co-founder of one of the largest Bitcoin mining pools in China (controls 11% of Bitcoin’s hash rate), just booked the first private Mars flyby on Starship. He travels via six different passports, lives part-time in the Arctic, and follows Mars time (24 hours, 37 minutes). Two-year mission.
Starlink Lunar Connectivity:
SpaceX is building gigabit connectivity for the moon. This is the beginning of the interplanetary internet (Vince Cerf’s vision). Laser connectivity between satellite swarms.
MAGNA MOPSTA:
Alex coined a new acronym for the top 11 companies at the heart of the singularity: Microsoft, Amazon, Google, NVIDIA, Apple, Meta, OpenAI, Broadcom, SpaceX, Tesla, Anthropic.
“I spoke to Elon at the Breakthrough Awards and we made the comment that this is the most energy released by a human machine other than a nuclear bomb.”
— Peter Diamandis
HERE’S THE BOTTOM LINE…
This week felt like institutions finally waking up to the reality that AI is not a technology shift. It is an anthropological shift. The Vatican is staking positions. Governments are choosing speed over safety. OpenAI and Anthropic are posting numbers that look like entire economies. Solo founders are doubling quarter over quarter. And SpaceX is launching the most powerful rocket in history while a crypto billionaire books a Mars flyby.
Salim nailed it: this is the first technology that forces us to define humanity. We used to ask what machines can do. Now we have to ask what humans should be FOR. If machines can write, reason, diagnose, optimize, and persuade, then human value cannot be based on productivity. The meaning of life cannot be “I output more than the machine.”
This is why I keep saying: do not get a job. Build a job. We just launched the Build with Gemini XPRIZE, $3 million from Google, challenging teams to pick a problem impacting 100,000 people and build something that generates revenue in three months. Over 2,000 registered in the first 24 hours. Rather than booing AI,
use
it.
The best way to predict the future is to create it yourself. And right now, we have the tools. They are completely democratized and demonetized. Go build.
Catch the full
Moonshots
episode on YouTube, and if you have not yet,
subscribe to the channel
. We just passed 500,000 subscribers.
See you next week,
Peter
This Week’s
Moonshots
Links
POPE LEO WARNS OF AI RISKS IN 42,300-WORD ENCYCLICAL
https://www.nytimes.com/2026/05/25/world/europe/pope-leo-encyclical.html
ANTI-DOOMER PUSHBACK DELAYS WHITE HOUSE AI EXECUTIVE ORDER
https://www.axios.com/2026/05/21/trump-ai-executive-order-postponed-why
DEEPSWE: FRONTIER LABS PERFORMANCE
TOKEN PRICES ARE FALLING, TOKEN DEMAND IS RISING
FRONTIER LABS REVENUE EXPLODES
https://www.theinformation.com/articles/openai-held-1-billion-revenue-lead-anthropic-first-quarter
DEEPMIND’S LLM MATCHES SUPERFORECASTER PERFORMANCE
TECH IMPACT ON JOBS & ECONOMY
https://futurism.com/artificial-intelligence/99-percent-ceos-workers-ai-survey
https://www.businessinsider.com/nvidia-ceo-jensen-huang-ai-job-cuts-losses-lazy-narrative-2026-5#:~:text=Nvidia%20CEO%20Jensen%20Huang%20criticized,in%20an%20interview%20on%20Monday.
https://www.cnbc.com/2026/03/31/oracle-layoffs-ai-spending.html
https://www.reuters.com/business/world-at-work/cloudflare-cut-over-1100-jobs-2026-05-07/
SAM ALTMAN WALKS BACK AI ‘JOB APOCALYPSE’ WARNINGS
https://www.reuters.com/world/asia-pacific/openais-altman-says-ai-unlikely-lead-jobs-apocalypse-2026-05-26/
SOLOPRENEURSHIP RISES IN Q1 2026
SPACEX LAUNCHES MASSIVE STARSHIPS V3 TEST FLIGHT
CRYPTO BILLIONAIRE BOOKS SPACEX’S FIRST PRIVATE MARS FLYBY
https://www.space.com/space-exploration/private-spaceflight/this-cryptocurrency-billionaire-will-fly-spacexs-1st-private-starship-to-mars-but-when
STARLINK ANNOUNCES PLANS FOR GIGABIT LUNAR CONNECTIVITY
NASA ADMIN. ISAACMAN EXPECTS CHINA CREWED MOON FLYBY IN 2027
https://spacenews.com/isaacman-expects-chinese-crewed-mission-around-the-moon-in-2027/
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TLDR:
The two mindsets that will separate those who thrive from those who stagnate in the next decade are
Purpose
(not passion) and
Curiosity
. Purpose is your destination. Curiosity is your rocket fuel. Together, they're how you take agency over a future that's accelerating faster than any institution can prepare you for.
I was on Ed Mylett’s podcast last week, and his son had sent in a question: “What prediction do you have about the next 10 years that most people will think is crazy?”
I rattled off the usual list. Reversing aging. A billion humanoid robots. Ambient AI that anticipates your needs before you know you have them. The economy going through the roof.
But Ed pushed back. He pointed out that at every college graduation this year, the moment any commencement speaker mentioned AI, the crowd booed. Poor Eric Schmidt got hammered. These are 22-year-olds who just spent $200,000 on a degree, and they’re terrified the world they prepared for doesn’t exist anymore.
They’re not wrong to be scared. They’re wrong to be passive. (And, IMHO, their wrong to blame AI rather than the institutions selling them a degree that’s now less useful.)
THE TWO MINDSETS THAT ACTUALLY MATTER
I teach a lot about mindsets. Your mindset determines how you deal with a challenge or an opportunity. And looking at the decade ahead, two mindsets matter more than anything else: purpose and curiosity.
Let me draw a distinction most people miss.
A passion is something you love doing. A purpose is something you love doing
that helps other people
.
That second part changes everything. Passion can be selfish. Purpose is directional. It connects you to the world. It gives you fuel that doesn’t burn out when things get hard.
My first purpose was opening the space frontier. I was nine years old, watching Apollo 11 on a tiny TV, completely infected with what I call “space religion.” That purpose launched Zero Gravity Corporation, Space Adventures, the XPRIZE Foundation, a dozen other ventures over the following decades. All from one kid in the Bronx staring at a screen in 1969 thinking, “I need to get up there.”
Purpose evolves. Today my purpose (my MTP) is this: to inspire and guide entrepreneurs and humanity to create a hopeful, compelling and abundant future. I haven’t eliminated my earlier purpose, but I’ve layered on top of it. You can too. Your purpose at 25 doesn’t have to be your purpose at 45. Shouldn’t be, actually.
Today there are so many amazing problems that need solving. I’m reminded of two truths I hold absolute:
“The world’s biggest problems are the world’s biggest business opportunities.”
“If you want to become a billionaire? Help a billion people.”
Here’s what makes this so urgent right now: the old social contract: “Do well in high school, get into a good college, get a degree, get a job”… is cooked. That playbook worked for the industrial age, when we needed factory workers and middle managers.
Today, the biggest unemployment pocket is ages 22 to 28. Recent graduates who spent hundreds of thousands of dollars on degrees that aren’t landing them the jobs they expected.
If you can’t afford a car, can’t afford a home, and don’t get married because of that, you’re angry. And I get it. I have 15-year-old twin boys. I think about their future constantly.
But the answer isn’t to rage at AI. The answer is to find your purpose and use these extraordinary tools to pursue it.
YOUR AI IS THE GREATEST TEACHER EVER BUILT
Ed said something on the podcast that was generous but accurate: he said, “You may be the most curious person I know.” I’ll take the compliment. But here’s the thing: curiosity isn’t some innate gift. It’s a practice. And right now, you have the most infinitely patient teacher in history sitting in your pocket.
If you’re listening to a podcast and someone mentions quantum computing and you don’t know what that means? Stop. Open Claude, or Gemini, or ChatGPT. Say: “Explain this to me like I’m five.” Then ask a follow-up. Then another. Go down the rabbit hole.
For most of human history, curiosity was a luxury. You were too busy trying not to die. Now you can know anything. The only barrier is whether you bother to ask.
A GUY IN MOROCCO, A FREE AI, AND A BUSINESS
I was in Morocco with my family recently. Small village. The hotel concierge introduced us to a young man who ran e-bike tours of the area.
We stopped at his home for Moroccan tea, and I asked: how did you start doing this?
No income. No connections. No business degree. He got on ChatGPT, described his skills, his situation, his village. Brainstormed with the AI. The e-bike tour idea came out of that conversation. Today it’s a thriving business that supports his family and connects tourists with a place they’d never otherwise see.
That guy had the same access to business strategy that a Harvard MBA has. He just needed curiosity and a purpose. That combination, plus a free AI tool, built him a life.
“The cost of starting a business has plummeted by 100x over the past decade… ten years ago you needed a coder, a lawyer, a web designer, an ad agency… today you need a purpose and a prompt.”
This is happening everywhere. Solopreneur formation has surged over the past year as AI tools have gotten powerful enough to replace entire teams. Five years ago, if you wanted to start a business, you needed a lawyer, web designer, financial advisor, ad agency, sales team. Today your AI handles all of it. You just need to know what problem you care about solving.
THE WALL-E FUTURE VS. THE STAR TREK FUTURE
I recently wrote about what I call the
five forks of humanity
here on my Substack. The first and most important fork: consumer versus creator.
Technology is going to take care of everyone to some degree. Some version of universal basic income is likely coming. Your Optimus robot brings you a beer. An AI-generated Netflix series stars you and your friends. Comfortable. Passive. Slowly atrophying. That’s the
WALL-E
future.
Then there’s the creator path. You use these same tools to build, solve, create things that didn’t exist yesterday. That’s
Star Trek
.
We just launched the Build with Gemini XPRIZE at Google I/O on May 19th: a $2 million global hackathon designed specifically for non-coders. Pick a problem that affects 100,000 people. Describe your solution in plain English. Let AI agents code it. Market it. Whoever generates the most revenue in 90 days wins. There’s a $500,000 grand prize, plus multiple $100,000 and some fifteen $50,000 prizes.
The point isn’t the prize money. The point is proof: anyone, anywhere, with purpose and curiosity, can build something real. Go to
www.geminixprize.com
.
WHAT THIS MEANS FOR YOU
If you’re an entrepreneur:
AI has collapsed the cost of starting a business by 100x. The barrier isn’t capital or connections anymore. It’s whether you have a purpose worth pursuing and the curiosity to figure out the how. Start with one conversation with your AI today.
If you’re an executive:
The number one mistake I see? Not being willing to try. Describe your entire business to an AI. Ask your AI “how Elon Musk or Steve Jobs would advise me?” Ask how to 10x your revenue. The quality of your business is the quality of questions you ask.
If you’re an investor:
Watch the solopreneur explosion. Companies built by one person, powered by AI agents, are surging. The next billion-dollar company may have exactly one employee.
If you’re a student:
That degree isn’t worthless, but it’s not enough. Find a problem you care about. Use AI to brainstorm and build a solution. The Build with Gemini XPRIZE at
www.geminixprize.com
is literally designed for you.
If you’re a parent:
When your kids leave for school, stop saying “get good grades.” Say “ask great questions today.” I say this to my boys every single morning. The quality of their future depends on it.
To a future of Abundance,
Peter
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I’ve been thinking a lot about wisdom lately. Not intelligence. Not speed. Not raw compute.
Wisdom.
The thing we used to reserve for grandparents, philosophers, and tribal elders who’d seen enough life to know which paths lead to ruin.
Here’s how I define it: wisdom is probabilistic pattern recognition across a vast number of lived experiences. When you go to the village elders and ask, “Which direction should I take?”, they don’t run equations. They draw on decades of watching people make choices and living with the consequences. They say, “If you go this way, based on everything I’ve seen, it won’t end well. Go this other way, and you have a real chance.”
That accumulated experience, compressed into judgment, is what we call wisdom.
Now ask yourself: what happens when an AI can simulate not hundreds or thousands of scenarios, but billions? When it can replay the entire observable history of human decisions, test every fork in the road, and tell you with quantified confidence which path has the highest probability of success?
I think that’s wisdom. And I think we’re watching it be born…
THE FIRST PROOF POINT: FUTURESIM
On this week’s
Moonshots
podcast, we discussed a new benchmark called
FutureSim
, built by a group of independent researchers. The architecture is brilliant: it replays the internet day by day starting from January 1, 2026, gives AI agents access to real news as it unfolds, then asks them to forecast real-world events 90 days out, with no web access beyond the replay window. The models can’t cheat by looking up what actually happened.
GPT 5.5 running Codex scored 25% accuracy, the highest of any frontier model tested
. It beat Polymarket crowd predictions on the Super Bowl.
Twenty-five percent doesn’t sound like much until you think about what’s being measured. These aren’t binary yes/no questions about coin flips. These are complex geopolitical, economic, and social events with thousands of variables. And 25% is the
floor
. This is the worst these models will ever be.
The Forecasting Research Institute estimates a 74% probability that an AI model will win a major sanctioned forecasting tournament by the end of 2026. We’re months away from AI beating the best human superforecasters on the planet, people who’ve spent their entire careers learning to predict the future.
“Remember Psychohistory from Asimov’s Foundation novels? Hari Seldon invents a mathematical theory that can predict the collapse of the Galactic Empire. FutureSim is benchmarking exactly that capability. And (as a reminder) this is the worst Psychohistory models will ever be.”
(Alex Wissner-Gross, Moonshots Podcast)
FROM PREDICTION TO PRESCRIPTION
But here’s where it gets really interesting. Prediction is only half the equation. The other half is prescription.
If you can predict outcomes, you can also test interventions. Alex made an analogy to medicine that stuck with me: “If you have a perfect digital twin of the system you’re trying to fix, you can exhaustively test all possible interventions to get from the bad state to a good state.” That’s how we’ll cure disease. AI builds a virtual cell, simulates every possible drug interaction, and identifies the optimal treatment. DeepMind’s AlphaFold already solved the 50-year protein folding problem. That work won the Nobel Prize in Chemistry. And that was just the opening act.
Now scale that to economies. To climate… to geopolitics.
Imagine a presidential cabinet meeting where, before making a policy decision on tariffs or energy infrastructure or immigration, an AI runs a billion Monte Carlo simulations of the downstream effects over 10 years. Not one analyst’s opinion. Not a committee’s consensus. A billion simulated futures, ranked by probability and human impact.
That’s not intelligence. That’s wisdom.
Salim Ismail, who’s been building what he calls the “Organizational Singularity” framework, immediately connected this to the boardroom: “This is incredibly powerful. You go from quarterly updates to real-time sensing.” Salim’s EXO architecture already integrates predictive AI into organizational decision-making, and the results are striking. Companies using these tools are running circles around competitors still relying on human-only forecasting cycles.
THE FINANCIAL SINGULARITY
Dave Blundin, who teaches AI ventures at MIT and has been investing in AI companies for decades, took it to its logical extreme on the podcast. Right now, thousands of hedge funds specialize in different sectors: semiconductors, retail, energy, biotech. Each employs hundreds of analysts poring over data.
“That entire industry could collapse into one or two AI models. If the AI is fundamentally better at picking markets, it’s not going to sit there and do one market. It’s going to expand across all markets. You’re going to see a collapse into just a couple of mega-funds with massive AI budgets.”
(Dave Blundin, Moonshots Podcast)
I called it what it is: “the financial singularity.”
Think about what that means. The collective wisdom of every analyst on Wall Street, every quant model, every earnings call, every SEC filing, every macroeconomic indicator, all compressed into a few AI systems that never sleep, never panic-sell, and process information at speeds humans can’t comprehend. Morgan Stanley’s 2026 outlook already describes this as a period of “creative destruction” in the hedge fund industry.
And it’s not just finance. The same pattern will play out in law (predict case outcomes), medicine (predict treatment efficacy), urban planning (predict infrastructure needs), and national defense (predict adversary actions). Any domain where wisdom, the ability to anticipate consequences across complex systems, creates value.
“While prediction markets like Polymarket and Kalshi are actively tracking >600,000 active predictions, we can expect AI with ultimately replace human opinion with AI-quantified probabilities.”
(Peter H. Diamandis)
WHY THIS MATTERS MORE THAN AGI
Here’s my contrarian take: we spend too much time debating when we’ll achieve AGI (artificial general intelligence) and not enough time recognizing that artificial wisdom may be more consequential.
Intelligence solves problems. Wisdom tells you which problems to solve, and which solutions will create new problems worse than the original. Intelligence builds a nuclear reactor. Wisdom decides where to put it and what safeguards to build around it.
I’ve spent 30 years working on “Moonshot problems.” At
XPRIZE
, we’ve launched over ~$600 million in prize competitions (delivering more than $30 billion in R&D) to solve humanity’s grand challenges. What I’ve learned is that the hardest part is rarely the technical solution. It’s knowing which approach won’t backfire. It’s the judgment call. It’s what you learn from watching a hundred teams try and fail.
When I sat down with Elon at Giga Texas last December, we talked for three hours about abundance and the future. One theme kept surfacing: the decisions we make in the next few years about AI governance, energy policy, economic redistribution, and space infrastructure will compound for decades. We don’t get do-overs.
What if the most important thing AI gives us isn’t speed or productivity? What if it’s the ability to see around corners? To test our decisions before we make them? To finally have, for the first time in human history, something approaching civilizational wisdom?
Asimov imagined Psychohistory as science fiction. FutureSim just scored 25% accuracy, and it’s improving fast. The gap between fiction and reality is collapsing.
HERE’S MY ASK
If you’re a CEO, start demanding predictive AI in your decision-making process. Not as a novelty. As the default. Every major decision should come with simulated outcomes. Every strategic plan should be stress-tested against a billion scenarios.
If you’re in government, pay attention. The tools to simulate the consequences of your policies before you implement them are arriving now. Use them.
And if you’re a parent, an investor, or just a person trying to figure out what’s next: take comfort in this. We’re not flying blind into the future anymore. AI won’t just make us smarter. It will make us wiser.
That’s a future worth building toward.
Catch this week’s full
Moonshots
episode wherever you get your podcasts. And join me and the Moonshot Mates at the Moonshots Gathering in Los Angeles on September 25th. Register at
www.moonshots.com
.
See you soon,
Peter
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### HYPE hits ATH, BTC ETF outflows continue, US strikes Iran

#### Crypto

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* [Fear & Greed: 30](https://www.coinglass.com/pro/i/FearGreedIndex) [|](https://www.coinglass.com/etf) [24h Liq: $301m](https://www.coinglass.com/liquidations)

* [BTC ETFs: -$125M | ETH ETFs: -$18M](https://www.coinglass.com/etf)

(truncated — read full post on source)

In 2003, a friend of mine gave me a book titled
Power of 10: The Once-A-Week Slow Motion Fitness Revolution
. At the time, I had some back pain and had not been working out much with weights while recovering. Combine that with extensive travel after leaving Morgan Stanley to start my own business, and I did not have much time to get on a regular workout routine.
Twenty-three years later, I am still working out with the same trainer doing slow-motion heavy weight training.
I have mentioned this before in the context of High Intensity Interval Weight training and HRV. This time I wanted to specifically emphasize the importance of slow motion. If you do 25 push-ups at normal speed and then try 25 at slow motion, you will feel the difference in the intensity. The important point is that this is not easy exercise. It is not stretching with weights or a low-intensity wellness routine. The
Power of 10
approach uses heavy resistance, but it changes the way that resistance is experienced. By moving the weight extremely slowly, the muscle is forced to stay under continuous tension. There is no momentum, no bouncing, and no escaping the hard part of the movement. The weight may not look explosive from the outside, but internally the effort is intense.
That is what makes the method so interesting. It combines the muscular demand of heavy resistance training with the control of slow movement. The goal is to create a very deep stimulus without the jerking, acceleration, and joint stress that often come with traditional heavy lifting. In other words, the workout is heavy and intense, but it is also controlled, forcing you to remain present and aware. That combination is what makes it such a useful lens for thinking about HRV.
Most people think about strength training as a muscle story.
You lift weights. The muscle breaks down. The body repairs. Over time, you get stronger.
That story is true, but it is incomplete. Resistance training is not only a mechanical event. It is also a nervous-system event. Every rep is a negotiation between effort and control, tension and relaxation, stress and recovery. The body is not just asking, “Can I move this weight?” It is also asking, “Can I stay organized while I am under stress?”
That is why slow-motion aware resistance training is especially powerful for heart rate variability, or HRV. HRV is not really a muscle-growth metric. It is a window into autonomic regulation. It reflects the body’s ability to move between sympathetic activation, the fight-or-flight side of the nervous system, and parasympathetic recovery, the rest-and-digest side. A healthier nervous system is not one that is always calm. It is one that can mobilize when needed and then return to calm efficiently. HRV is one way to observe that flexibility.
This is where slow-motion training becomes more interesting. The benefit is not simply that slow reps create more time under tension. The deeper benefit is that slow reps create more time under awareness. They force the lifter to feel effort as it is happening. They expose breath-holding, facial tension, bracing patterns, panic, impatience, and the urge to escape discomfort. In that sense, slow training is not just strength training. It is autonomic training.
The goal is not to turn lifting into meditation. The goal is to turn lifting into controlled stress practice.
HRV Is About Regulation, Not Just Fitness
A high HRV is often associated with better recovery, stronger vagal tone, and more adaptable autonomic control. But HRV should not be treated like a score to chase every morning. It is a signal. It moves around because the body is constantly responding to sleep, stress, hydration, food, alcohol, illness, training load, emotional state, and recovery.
That is exactly why it is useful. HRV helps reveal whether the body is adapting or merely absorbing stress. Exercise can improve HRV over time, but hard exercise can also suppress HRV in the short run. That is not a contradiction. It is the basic logic of training. Stress first. Recovery second. Adaptation third.
Systematic reviews have found that exercise interventions, including resistance training, can improve HRV and autonomic function over time. But the mechanism is not magic. Exercise gives the body repeated opportunities to practice moving from activation back into recovery. Over time, that can improve resting heart rate, vascular efficiency, baroreflex function, and parasympathetic rebound.
This is the central point: the workout itself does not raise HRV in the moment. In many cases, it lowers it temporarily. The improvement comes from the body learning how to recover from the stress.
Why Slow Motion Changes the Stimulus
Most people lift too quickly. They use momentum, rush through the uncomfortable part, lose position, and turn the set into a performance of completion rather than a practice of control.
Slow-motion training changes the entire experience. When the concentric and eccentric portions of a lift are slowed down, the muscle stays under tension longer. There is less opportunity to bounce, swing, or rely on momentum. The lifter has to control the weight through the entire range. That increases metabolic stress and motor-unit recruitment even when the external load is not extremely heavy.
But the more important shift is internal. Slow movement gives the nervous system fewer places to hide.
A fast rep can be escaped. A slow rep has to be inhabited.
That matters because the autonomic nervous system responds not only to load, but also to perception. If the body experiences effort as chaos, threat, and panic, the sympathetic response rises. If the body experiences effort as controlled, deliberate, and survivable, the stress signal changes. The same muscular work can feel very different depending on breath, attention, and pacing.
This is why slow-motion aware training has a unique place in HRV work. It brings together physical load and nervous-system regulation. The lifter is not just asking the body to produce force. The lifter is teaching the body to produce force without losing control.
Breath Control Is the Bridge
Breathing is the bridge between voluntary control and autonomic function.
You cannot directly tell your heart to beat with more variability. You cannot simply command your vagus nerve to become stronger. But you can control your breathing. That makes breath one of the few levers through which conscious behavior can influence autonomic state.
HRV biofeedback research is built around this idea. Slow, paced breathing near a person’s resonance frequency can increase heart rhythm oscillations and may improve baroreflex sensitivity, which is part of the body’s blood-pressure regulation system. Reviews of slow-paced breathing and HRV biofeedback describe the interaction between respiration, vagal activity, heart rhythm, and baroreflex function as a major pathway for improving autonomic balance.
Slow-motion resistance training is not the same as HRV biofeedback. But it can borrow from the same biology.
When a lifter moves slowly and breathes deliberately, the set becomes a practice in staying regulated under load. The person learns not to hold the breath unnecessarily. They learn not to tighten the jaw, neck, and face as soon as effort rises. They learn to exhale through the hard portion, inhale under control, and maintain awareness as fatigue builds.
This is where the “aware” part of slow-motion aware training matters. Without awareness, slow training is just a tempo technique. With awareness, it becomes a stress-regulation technique.
The body is being taught a simple lesson: effort does not have to equal panic.
Read more

I started this Substack because I wanted to take you along on my own journey of raising my HRV, not just as a metric on a wearable, but as a window into how the entire body is functioning. One of the most powerful things about using AI to solve a health problem is that it does not look at symptoms in isolation. At its best, AI acts like a polymathic thinking partner. It can connect clues across physiology, immunology, sleep, stress, the gut, the nervous system, and recovery to help reveal the bigger picture. That is exactly what happened with seasonal allergies.
For years, I hated this time of year. The congestion, itchy eyes, brain fog, and constant need for allergy medicine felt inevitable. But as I focused seriously on raising my HRV, something unexpected happened: my seasonal allergies went away. I do not get them anymore. AI helped me understand why. Allergies are not just about pollen. They are often a sign that something deeper in the system is out of regulation. So this week, I want to use allergies as the first example of a larger idea: the same tools that raise HRV may also create benefits far beyond your heart rate data. They may help your body become less reactive, more resilient, and better able to respond to the world without overreacting to it.
This time of year, so many people are suffering quietly. They wake up with swollen eyes, stuffy noses, scratchy throats, headaches, fatigue, and the kind of brain fog that makes ordinary work feel harder than it should. They check pollen counts like weather alerts and brace for the familiar seasonal ambush.
And yes, pollen matters. Tree pollen generally drives spring allergies, grasses tend to rise later, and ragweed and other weeds fuel the fall wave. Mold can add another layer when heat and humidity climb. But pollen is not a pathogen. It is not a virus. For many people, it is biological dust that the body has decided to treat like an emergency.
This is where Heart Rate Variability becomes interesting.
Raising HRV will not magically cure allergies, and it is not a reason to ignore asthma symptoms, stop appropriate medication, or skip an allergist. But HRV gives us a window into a bigger truth: seasonal allergies are not only about what is in the air. They are also about the state of the system receiving the signal. When your nervous system is chronically braced, underslept, inflamed, overstimulated, and under-recovered, your immune system is more likely to overreact. I like to say, if your immune system is “drunk,” it can’t see toxins clearly.
Clinically, allergic rhinitis is an IgE-mediated immune response to inhaled allergens, often producing sneezing, congestion, itching, and a runny nose. In Type I hypersensitivity reactions, IgE can activate mast cells and basophils, which release inflammatory mediators including histamine.
But the deeper question is: why does one body tolerate pollen while another detonates in its presence?
Read more

Introduction: Awareness Is the Foundation of Health
“You have power over your mind, not outside events. Realize this, and you will find strength.”— Marcus Aurelius, Meditations
One of the most important, and often overlooked, drivers of long term health is awareness. Not in the abstract sense, but in the ability to see patterns in how your body responds to the inputs of daily life. Sleep, nutrition, stress, alcohol, training, and travel are not isolated events. They are part of a system, and over time, they leave a measurable signature. This is where the Oura Ring has been so valuable for me. It does not simply track metrics. It reveals patterns. It shows how certain behaviors influence heart rate variability, or HRV, not just on a given day, but across multiple days and even weeks. The goal is not to control every variable or to maintain a perfect score. The goal is to recognize what consistently moves your system in the right direction, and what quietly pulls it away.
In that context, one of the most powerful insights is understanding that some of the most meaningful influences on HRV are not obvious. They are embedded in normal routines, which makes them easy to overlook. Travel is one of the clearest examples. It feels routine. It is often necessary. Most times, it is out of our control. But once you begin to see the pattern, it becomes clear that travel has a consistent influence on HRV, not only during the experience, but also in the recovery period that follows. This is the importance of solving for a problem using a systems thinking approach. Many times, especially with health, it is not just about what you are doing for you but how you are navigating stressors for work. Soon I will also go through the commute which was an eye opener for me.
There is also an important mindset embedded in this. Having access to this kind of data creates a choice. You can either engage with it and learn from it, or you can ignore it. Choosing not to measure or observe does not remove the impact. It simply removes the visibility. In that sense, opting out of awareness is, in itself, a decision. And over time, that decision compounds just as much as the behaviors being measured. I recognized that as I got older, all these little things add up if you want to focus on anti-aging.
Understanding HRV and the Recovery System
HRV reflects the balance between the parasympathetic nervous system, responsible for recovery and repair, and the sympathetic nervous system, which supports alertness and responsiveness. A higher HRV generally indicates a system that is adaptable and well recovered, while a lower HRV signals that the body is allocating more resources toward managing current demands.
Travel influences this balance in several ways at once. It changes sleep timing, introduces new environments, alters movement patterns, and often shifts nutrition and hydration. Together, these factors move the body toward a more activated state. The key is not the presence of activation itself. This is a normal and necessary part of life. The key is how efficiently the body returns to a recovered state afterward.
Why Travel Matters More Than We Realize
Travel is so embedded in modern life that it is rarely considered in the context of recovery. Flights, commutes, and trips are treated as routine logistics rather than physiological inputs. However, wearable data consistently shows that HRV tends to move lower during travel periods. More importantly, the effects often extend beyond the travel itself.
A single day of travel may be followed by multiple days where HRV gradually returns to baseline. During this period, sleep quality may still be adjusting, resting heart rate may remain slightly elevated, and overall readiness may not fully normalize. Over time, these periods can accumulate, shaping the broader trend of HRV across weeks and months.
Understanding this pattern is where awareness becomes valuable. Once you recognize that travel influences more than just the day it occurs, you can begin to plan around both the experience and the recovery window that follows.
Why the Effects Can Last After You Return
The extended influence of travel on HRV is driven by several overlapping factors. Circadian rhythm plays a central role. Even without crossing time zones, changes in sleep timing and light exposure can shift the body’s internal clock. When time zones are involved, this adjustment becomes more pronounced and may take several days to fully realign.
In addition, travel introduces a steady stream of inputs that require attention and adaptation. Navigating airports, adjusting to schedules, sitting for extended periods, and engaging with new environments all contribute. Each of these adds to the overall load the body processes. While none are extreme on their own, together they create a meaningful shift in how the system operates.
Behavioral patterns during travel also contribute. Meals may be less consistent, hydration can be overlooked, and alcohol is often more common. These factors influence sleep quality and recovery, which in turn affect HRV. Alcohol, in particular, tends to reduce overnight HRV and elevate resting heart rate, extending the time it takes for the body to return to baseline.
Why Aging Changes the Equation
As individuals get older, the body’s recovery processes become more deliberate. Baseline HRV may gradually decline, and the time required to adapt to changes, such as travel, can increase. What might have been a quick adjustment earlier in life can evolve into a more extended recovery period.
This does not mean travel becomes problematic. Rather, it highlights the importance of approaching it with intention. Managing the inputs around travel becomes a way to support the body’s ability to adapt and return to equilibrium efficiently.
Read more

Despite openly admitting that I seldom read books anymore due to AI, I am often asked for book recommendations to help people navigate the transition we are living through with artificial intelligence. The funny thing is that the book I usually recommend is not a technical book, a futuristic book, or a manual on AI. It is
The Daily Stoic
by Ryan Holiday and Stephen Hanselman.
That may seem strange at first, but the reason is simple. Change is hard for humans because our biological brains are built to seek comfort, safety, pattern recognition, and stability. Exponential change is even harder because our linear brains are not naturally wired to process a world that is moving faster, compounding faster, and forcing us to adapt faster than at any point in our lives. For that challenge, philosophy may be more useful than technology.
This is also why I have been thinking more about how to expand my own work. On my YouTube channel, I have spent the last two years trying to help investors understand macro, markets, AI, and the speed of technological change in a broader framework. Soon, I plan to add a dedicated channel focused on the crypto ecosystem, but not in the narrow way crypto is often discussed. The goal will be to look at crypto the way traditional investors look at the macro world: through liquidity, flows, infrastructure, adoption, regulation, incentives, network effects, and the changing architecture of the financial system. My belief is that a major shift is coming driven by the parabolic agentic rise and asset bridge of tokenization, and to see it clearly, investors need to stop treating crypto as a collection of isolated tokens and start analyzing it as an emerging financial ecosystem being pulled forward by AI, agents, stablecoins, tokenization, and programmable settlement.
The Daily Stoic
, published in 2016, is a modern, day-by-day guide to Stoic philosophy, using short passages from thinkers like Marcus Aurelius, Seneca, and Epictetus to help readers build discipline, perspective, patience, and emotional control. Its lasting power comes from the reminder that humans have been wrestling with the same internal anxiety, fear, ambition, frustration, and chaos for thousands of years. That was true even in eras when life expectancy was far shorter and the external problems of war, disease, hunger, political instability, and survival were far greater than most people can imagine today. The tools change. The headlines change. The assets change. But the inner experience of uncertainty does not change nearly as much as we think.
In the book, there is a simple line that captures one of the hardest parts of investing: “Games and seasons are constituted by seconds.” The point is not that the long term does not matter. It is that the long term is not experienced as a clean, elegant chart moving from the lower left to the upper right. It is experienced one second, one day, one headline, one drawdown, and one emotional test at a time.
That is especially true in crypto today. The long-term story has arguably never been clearer. Artificial intelligence is accelerating. The agentic revolution is moving from theory to workflow. Stablecoins, tokenization, and digital financial rails are becoming necessary infrastructure for a world where software agents, humans, institutions, and machines need to transact globally and continuously. And yet, despite that clarity, the daily experience often feels like a depressing game of whack-a-mole. Every time the thesis feels obvious, price action fails to confirm it. Every promising development is met with another rotation, another liquidation, another regulatory headline, another failed breakout, or another reminder that markets do not move on our preferred timeline.
That emotional disconnect is the real challenge. As investors, we want the future to announce itself in the price today. We want certainty in the long-term thesis to produce certainty in the short-term chart. But markets rarely work that way. The AI buildout is visible because it is physical: chips, data centers, power, cooling, networking, memory, and electricity. The crypto buildout is less visible because much of it is financial plumbing: settlement rails, tokenized assets, stablecoin liquidity, custody, compliance, wallet infrastructure, identity, and guardrails for agentic activity.
The fact that this infrastructure is harder to see does not make it less important. If anything, it may make it more important. A world of AI agents cannot scale on yesterday’s financial system alone. Agents need money. They need permissions. They need settlement. They need trusted rails. They need financial guardrails that allow autonomous or semi-autonomous economic activity without chaos. That is a bullish foundation for crypto, even when daily prices refuse to reward the patience required to see it.
The other important distinction is that the physical AI buildout will inevitably face bottlenecks because it is constrained by atoms. Chips, power, cooling, transformers, networking equipment, memory, land, permits, and skilled labor all depend on real-world supply. Even if demand is obvious, the supply response cannot instantly appear. Crypto is different. Once agent-driven financial activity begins to accelerate, the growth can behave much more like software than infrastructure. Stablecoins, tokenized assets, wallets, smart contracts, and settlement networks can scale through code, liquidity, and adoption rather than waiting years for factories, transmission lines, or power plants. That does not mean there will be no constraints around regulation, security, or trust, but it does mean the upside can express itself through network effects in a way that looks far more like the software platform bull market than a traditional industrial cycle.
That is why the eventual shift in investor attention could be so powerful. The market has already shown this year how quickly investors can respond when token usage, stablecoin volumes, or network activity begins to validate a thesis. Once investors start to see AI agents not only as users of compute but as future users of money, settlement, identity, and financial permissions, the narrative can change quickly. In the physical AI buildout, demand can run into supply. In crypto, demand can compound through usage. When that happens, the same investors currently frustrated by the lack of price confirmation may suddenly realize that the financial guardrail layer has the potential to produce the type of parabolic network-effect moves they once associated with software platforms, and earlier generations associated with crypto itself.
At the same time, price can never be ignored and right now, it is saying investors are not focused on the upside with crypto. The market is a real-time summary of the emotions, beliefs, positioning, liquidity needs, and time horizons of every participant. It is not always right about the future, but it is always telling you something about the present. That distinction matters. A market that refuses to go up despite good news may be telling you that belief has not yet converted into urgency. A market that sells off on bullish developments may be telling you that positioning, leverage, or fatigue is still more powerful than the narrative. A market that starts rising and holds gains may be telling you that skepticism is being absorbed and demand is finally overwhelming supply.
The goal is not to fight the market simply because you believe you are right. The goal is to respect the long-term thesis while waiting patiently for the market to confirm that the trend is ready. Patience is not passivity. Patience is preparation. It is the discipline to do the work before the price action makes it obvious, and then have the confidence to get more excited when the market itself begins to agree.
This is where the Stoic framing matters and why
The Daily Stoic
has been a part of my daily reading since 2017. The Stoic does not deny discomfort. The Stoic does not pretend volatility is pleasant or that waiting is easy. The Stoic simply refuses to let the immediate problem erase the larger reality.
In markets, the immediate problem is always loud. A token is down. A stock is lagging. A breakout failed. A competitor rallied more. A narrative went quiet. Someone on social media declared the trade dead. All of this is happening while parabolas seem to be appearing everywhere except the place where many investors were trained to expect them first: crypto. These daily irritations create anxiety because they force investors to relitigate the thesis over and over again.
But the right question is not whether every day feels good. The right question is whether the underlying world is moving toward or away from the thesis. On that measure, the answer still points in one direction: more AI, more agents, more digital activity, more demand for programmable financial infrastructure, and more need for crypto-native rails. The mistake is allowing the daily price action to become more real than the structural shift. The equal mistake is ignoring the price action altogether. Reality requires holding both truths at once.
This does not mean every coin wins, every valuation is justified, or every dip should be bought without discipline. Stoicism is not blind optimism. It is realism. The bullish case for crypto must be paired with standards: real usage, credible networks, liquidity, security, regulation, institutional access, and integration into the emerging AI economy. It must also be paired with trend awareness.
The best investors do not need to buy every bottom or predict every turn. They need to understand the direction of the world, watch how the market is digesting that direction, and then wait for the moment when narrative, fundamentals, liquidity, and price begin to align. That is when patience starts to pay. That is when the market tells you it is time to get excited. Until then, the job is to observe without panic, prepare without forcing, and keep separating temporary frustration from permanent impairment.
At some point, the spotlight of momentum will begin to shift. For the seven months, the market has been obsessed with the agentic driven physical buildout of AI: semiconductors, data centers, power, cooling, networking, memory, and all the infrastructure required to turn intelligence into a scaled industrial system. That spotlight has been deserved. But the next phase of the AI story will not only be about building the machines. It will also be about building the financial guardrails that allow those machines, agents, businesses, and humans to interact safely, legally, and efficiently. That is where crypto, stablecoins, tokenization, and programmable settlement become harder to ignore.
The key is not to force that transition before the market is ready. As Marcus Aurelius reminds us in a passage often associated with Stoic discipline, “What stands in the way becomes the way.” For crypto investors, the thing standing in the way right now is the tape itself. Good news has not consistently been rewarded. Bad news has not yet been easily absorbed. Momentum has not fully confirmed that the market is ready to shift its attention from the physical AI buildout to the financial infrastructure layer. But that obstacle is also the signal. When good news starts to work, when bad news can be handled, when breakouts hold, and when the market begins rewarding the financial guardrail theme instead of dismissing it, that will be the tape telling us the season has changed. I think that day is coming soon. But until the market confirms it, the right posture is patience, preparation, and respect for the process.
So the discipline is to return to the process. Games and seasons are constituted by seconds. Markets and technological revolutions are constituted by days that often feel confusing, frustrating, and unrewarding. The AI buildout, the agentic revolution, and the construction of financial guardrails are the season. The daily candles are the seconds. If investors let every second determine their belief in the season, they will almost certainly lose focus at the moment when focus matters most.
The task is not to ignore price, but to put price in its proper place. Price is feedback, not final truth. Volatility is information, not destiny. Anxiety is a signal, not a strategy. The reality is that the world is moving toward more compute, more automation, more digital settlement, and more need for crypto-enabled financial infrastructure. The Stoic investor’s job is to hold that reality in view, brick by brick, while patiently waiting for the market to confirm when belief has become trend.

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

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