The Samsung Strike, the SK Hynix Defection, and Why Memory Stocks Went Vertical

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The year was 2020, I had just recently graduated and got my first job. I counted myself lucky for getting hire during those gloomy times. We were around three months into the pandemic and it was wrecking havoc all around the globe. There was panic and uncertainty everywhere, but not for me. The future looked bright and stable. I pride myself for managing to do the impossible during those times, get not just a whatever job, but one that had the three characteristics I was looking out the most for in a job: a good salary (I know this is subjective, but basically I could buy unnecessary things and still manage to have some spare money for savings), it was 100% traveling (I am part of that generation who was embedded the idea that they need to travel in order to be happy), and it was outside my country, because you know traveling feels better when it isn’t in your own country.
Now, fast forward 6 months into the job and I hated it. There was nothing about it that I enjoyed. At first it was the traveling part that got me all excited about that job, but after a while it just became tiresome, especially on COVID times with all the restrictions.
My job felt like a cage and with every passing day it was becoming harder to break out of it. I remember stumbling upon this quote from Taleb on one of his books that I was reading at the time and I understood why I felt that way “The three most harmful addictions are heroin, carbohydrates, and a monthly salary.” The false safety that having a monthly salary gave me was making me feel comfortable and turning me into a lazy person. My job didn’t feel fulfilling at all and I wasn’t interested in what I could learnt from it ether. I was just there for the money.
It was around that time too that I started reading about crypto, Web 3.0, NFTs, DeFi, etc. There’s something that I have to tell you about myself, I’ve never been genuinely interested in anything. Every subject in high school felt lame and boring for me; the only exception being History, I used to love and still love to learn about the past events of our world. In college it was the same. I chose mechatronics engineering as my major just because people told me that it was one of the most difficult careers and hey if it is difficult this means that when I am done with it I am going to be one of the smartest guys in the world and earn a lot of money right? Right?
Now here I was reading on the internet about this new class of assets and how this was the future. I started reading about bitcoin first to understand how it worked and what was the purpose of it. Then, after I was done with Bitcoin, I started reading about Ethereum and WOW, Ethereum was in a whole different level. Bitcoin was the first cryptocurrency and is definitely the most famous one, but there is not much you can do with. On the other hand, what you could do with the Ethereum blockchain was limitless, bounded only by our imagination as I see it; LP provider, ask for loans, yield farming, crypto gaming, NFTs, etc. I was hooked after this, I went down the rabbit hole and developed an addiction to reading about crypto and watching the price charts every single day. Every day that I didn’t read about some new project on twitter or discovered some new token felt like I was wasting my time. Crypto was the first topic in my whole life that I felt like genuinely interested in learning more about it. It was the first time that I was curious and wanted to learn more and more just for the sake of it, nothing like school where you are solely motivated or constrained by the need to have good grades, because in my case that was the only motive for me; I had to get good grades so that I could keep my scholarship. It was never for my own self interest.
From what I’ve learnt in this space and life generally, there is not just one way to make it. By making it I mean having enough money to have the freedom to do the things that you enjoy the most. The most obvious way to make it in crypto is probably buying a coin or token and getting a 100x or 1000x. So I started buying different tokens and coins from different blockchains, trying to keep up with the narratives that were out there and doing my best to rotate in time before everyone else dumped on old little me, this was the hardest thing to do. A free for all market it was and now I was part of it. Reminiscing about the past what hurt me the most during this past cycle was my greediness and not having a system to take profits. The culture of hodling my bag and never taking profits hold me back from making more money that I could. In hindsight it all makes sense; you need to take profits on the way up and not get too greedy, but when you some money in there and you see it double or triple in less than a week you star to feel all sort of emotions that blind your process of thought. That’s the reason why it is important to have a profits taking system, something I am aware of now and working towards developing.
Anyways, I did have a good run and manage to get enough money to get the courage to leave my old job, but it wasn’t without some struggle, specially mental struggle. It was around December 2021 when I finally gather the necessary courage to put myself through that conversation with my manager which, by the way, I was looking forward since the day I started my crypto journey. After all was said and done I felt free, finally I was going to have all the time in the world to put it into something that I enjoyed learning about. Little did I know that my happiness wouldn’t las forever.
After some relationship and personal problems I was left in a bad state of mind. I had many unresolved issues, traumas, that have to been deal with but couldn’t at the past because I wasn’t entirely aware of them. I never really put any thought to try to understand myself and what my triggers where, why I behaved it the way I did. That’s as far as I will go with this topic. Crypto didn’t help much with my state of mind either; the markets just started dumping and being the hopeless optimistic that I was at the time I didn’t sell any of my bags until my net worth was less than the half of what it was at its highest. Those were the darkest months of my life, never had I lost so much money in so small span of time. Well, to date I am still losing money, because I tried to trade every day just to get some of that money back. Whether I will lose it all or make it back is something that the future will tell. If I am being honest with myself I should stop now, I am no expert and what is most likely to happen is that I will lose it all. Things don’t look that good. It is now mid June and I’ve been gambling a lot lately with the money I was left with. I’ve also been a little bit reckless with my money. I’ve been spending more money that I should lately.
The thing that hits me the most is that I have no job right now and making my way out in crypto is not going to be a walk in the park. At the moment I am trying to motivate myself to learn new skills like writing, learning SQL and going through discords in order to get involved in projects, but it is something that I’ve never done before, so it’s been a little hard for me. At the same time I’ve been traveling a lot to get my mind off all this stress that is being cause by the uncertainty that I am experience.
I am just 26 years old and I shouldn’t stress, because stress does nobody any good, but sometimes it is so hard to keep myself calm and try to convince myself that better times will come, that after the rain the sun always shine. I am being a little dramatic, am I not? I know Life isn’t supposed to be easy at the end, those times when you are down give more meaning and make you appreciate more the good moments in life
Now, here I am 6 months after quitting my job and losing most of the money I’ve made in crypto. It’s been a hell of a ride, but I am aware that I am just getting started and every single thing that I’ve been through until now was either a lesson that I had to learn or an experience which I need to go through to set me up for the next chapter of my life.
I’d like to end with some parting thoughts. This article was never meant to be published, it was just to prove myself that I am also capable of writing and what is easier to write about than myself, because there is nobody that can do this better. I get scared because I want to be an analyst and being an analyst means putting your thoughts out there for people to read out loud and judge. They are going to judge every single detail of the research I’ll do and I am fine with it. This will help me become better every day. I know I can, I know I will.

Welcome to Mando’s Newsletter by me, Mando. Just your regular engineer trying to make it out there; we all deserve to be free; no financial advice.
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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.

It is that time of year again.
Time for the 152nd running of the Kentucky Derby.
Since I started my own business around AI, it has been impossible for me to find the free time I had in the past to commit to doing my annual handicap of the Derby. But every year, as we get closer to the race and friends and family start reaching out, I realize there is no way I can’t spend at least a full day diving into what remains the most exciting two minutes in sports.
Part of that is tradition.
Part of it is the challenge.
But a big part of it is personal.
My father, who passed away last year, taught me how to handicap races at a young age. More importantly, he taught me how to think, how to convert odds into probabilities, how to question consensus views, and how to combine data with human behavior.
That framework has carried over into everything I do today, from investing to building a business in AI to making everyday decisions.
Each year I write this, the goal is obviously to help you be profitable.
But more importantly, it is to help you enjoy the day.
The Kentucky Derby is one of the last true American events that still feels exactly as it should. It brings together friends and families from across the country for a day that feels like a reunion, a celebration, and a festival all wrapped into one.
If you have never been, I can’t recommend it enough.
It is like a prom, reunion, and Mardi Gras all happening at once and every 30 minutes it feels like New Year’s Eve as another race goes off. The Derby itself may only last two minutes, but the day lasts 480 minutes, and those memories last a lifetime.
This Year’s Derby: No Clear Favorite
This year is different.
I can’t remember a Derby I’ve handicapped where there wasn’t at least one horse that stood out clearly above the rest.
That is not the case this year.
This is a deep, competitive field where multiple horses can win depending on:
Trip
Pace
Positioning
And just a bit of racing luck
Even the traditional elimination frameworks don’t simplify the race the way they used to. The game has changed. Horses are more lightly raced. Training patterns are different. The old rules still matter, but they don’t dictate outcomes the same way anymore.
That’s what makes this race interesting.
Why I Handicap It Differently
Most people approach the Derby by asking:
“Who do you think will win?”
That’s the wrong question.
The right question is:
“What are the true probabilities, and where is the market wrong?”
Horse racing is one of the purest examples of a market.
The odds are not set by an analyst or a model, they are set by people. By narratives. By emotion. By bias. By momentum.
That’s no different than financial markets.
There are:
Stories driving attention
Data driving conviction
And pricing driven by expectations
The edge comes from finding where those expectations are off.
That’s the same way I approach investing. And it’s the same way I approach the Derby.
My 2026 Kentucky Derby Guide
I put together a full breakdown of this year’s race, including:
A complete horse-by-horse analysis
My fair odds for every runner
How I expect the race to unfold from a pace and positioning standpoint
And how I’m thinking about betting it
You can access the full report here:
👉 https://drive.google.com/file/d/1F-dfxMiba_qgKf-5R9j38OHVoXKG_N_a/view?usp=drive_link
Final Thought
This is one of those races where confidence should be lower but opportunity may be higher.
There is no obvious answer.
But that’s exactly where value tends to exist.
Enjoy the day.
Enjoy the race.
And if you’re betting it, think in probabilities, not predictions.
Good luck this weekend.
Jordi

This week on
Moonshots
we covered 10 stories shaping our future: from Google’s jaw-dropping earnings, to ocean-based data centers, to Sam Altman abandoning his own UBI experiment.
If you haven’t had a chance to listen to this week’s
Moonshots
episode or would like to remind yourself of the most important points, let’s dive in.
ARTIFICAL INTELLIGENCE
Google Is Eating Everything (And Still Hungry)
Alphabet posted $109.9B in revenue with 22% YoY growth and $62.6B in profit. Google Cloud hit $20B with 63% growth, outpacing both AWS and Azure.
The hidden story: search volume has been flat since 2017, but AI-powered ad targeting turns every model improvement into profit. Market cap is 4% from overtaking NVIDIA.
Even Google can’t build fast enough. Demis Hassabis admitted they’re compute-constrained. Inside Google, Search, Cloud, and DeepMind fight each other for new compute capacity.
“The future is a liquid market where the highest dollar-value-per-token wins. It’s called the Singularity for a reason.”
— AWG
The Pentagon Goes Shopping for AI
Seven frontier AI companies (including OpenAI, Anthropic, and Palantir) signed deals with the Pentagon.
600 Google DeepMind employees protested. Some unionized: a first in the AI industry, a bizarre juxtaposition of 19th century labor tactics protesting 21st century military AI.
The cultural rift is real. DeepMind in London is culturally separate from Google in Mountain View, and the friction is only growing as AI becomes embedded in national security infrastructure.
“AI is not just a tool now, it’s becoming a decision layer. You can understand the backlash. Navigating this is going to be crazy.”
— Salim
The OpenAI-Microsoft Marriage Is Over (Sort Of)
OpenAI ended Microsoft’s Azure exclusivity and is now running on AWS, Google Cloud, and Oracle. Microsoft starved it of capacity, so OpenAI started dating everyone else.
OpenAI missed its goal of a billion weekly ChatGPT users and several revenue targets. The CFO suggested waiting until 2027 for an IPO, admitting the company doesn’t meet reporting standards for public companies.
“Google figured out how to turn AI into revenue instantly. OpenAI hasn’t cracked that yet. Consumers don’t want to spend big on reasoning tokens. Enterprises do. Anthropic figured that out first.”
— Dave
China Blocks Meta’s Manus AI Acquisition: A Cold War Thriller
Meta thought it had closed its $2.5B acquisition of Manus in December 2025. China barred the founders from leaving the country and demanded the deal be unwound… even though employees, tech, and investor payouts were already completed.
Meta flew the Manus engineers out of mainland China to Singapore on a private jet in the middle of the night, knowing the deal would be blocked if they stayed.
China is leveraging its broader business relationship with Meta to force the unwind. Geopolitical pressure, not a legal dispute.
“When you decided seven years ago to work on AI, you didn’t know you were going to end up being a political prisoner candidate.”
— Dave
The March Toward AGI and a Debate About What That Even Means
Greg Brockman says we’re 80% of the way to AGI. Jack Clark gives recursive self-improvement a 60% chance by end of 2028.
Richard Dawkins says Claude may already be conscious: “If these machines aren’t conscious, what more could it possibly take?”
Brian Elliott’s hot take: transformers alone won’t get us to AGI. AWG pushed back: recent models have built entire compiler chains from scratch.
“If AI becomes conscious, you have a moral rights problem. If it becomes agentic, you have a governance problem. The governance problem comes first.”
— Salim
Blitzy Raises $200M: The AI Coding Revolution Goes Mainstream
Brian Elliott, CEO of Blitzy, announced a $200M raise on the pod. Dave revealed the valuation is north of $3B, up from $1.2B just a year ago.
Blitzy is building AI coding agents that rebuild enterprise software. Demand is insatiable.
Brian: “Jobs are bundles of tasks – the tasks shift, but humans keep providing relative value.” As Dave noted: with AI as a sidekick, hard skills are commoditized; soft skills are suddenly the scarce resource. It’s never been a worse time to be at a big tech company doing layoffs, and never been a better time to join a fast-growing AI startup.
DATA CENTERS
Data Centers Are Moving to the Ocean, Space, and Farmland
The compute hunger is so intense we’re running out of places to put data centers. Three stories paint the picture:
Ocean:
Peter Thiel is backing Panthalassa, floating data centers powered by wave energy with seawater cooling. $140M raised, $1B valuation, commercial deployment 2027. AWG thinks this could be the killer app for ocean colonization.
Space:
Star Cloud is raising $200M at a $2.2B valuation for orbital data centers powered by solar. They launched their first H100 into space in 2025 and plan to deploy 88,000 satellites.
Farmland:
67% of planned US data centers are now slated for rural areas, versus just 13% today.
“This is the biggest geographic wealth transfer since fracking. Whoever thought rockets would be part of the innermost loop? How high could this go? Higher.”
— Peter
EXPONENTIAL ECONOMY
Private Equity Becomes AI’s Trojan Horse
OpenAI finalized a $10B venture with TPG, Brookfield, and Advent. Anthropic launched a $1.5B venture with Blackstone, Goldman Sachs, and Hellman & Friedman.
PE firms control trillions across thousands of companies. AI is not entering corporations through IT departments. Instead, it’s coming top-down, mandated by owners who can bypass the corporate immune system.
Salim called it the organizational singularity: PE breaks the immune system because you can just mandate it, taking AI from chatbot experiment into EBITDA transformation.
If you’re running a business: either you become the PE firm that mandates AI adoption in your own company, or someone else will do it for you.
Sam Altman Abandons UBI and Proposes Something Better
After funding a three-year study, Altman found that UBI increased spending but didn’t improve health outcomes or healthcare access.
His new proposal: give people a stake in AI’s upside through compute access, equity, or a public wealth fund. Think the Alaska Permanent Fund, but for compute.
AWG: OpenAI already has hundreds of millions using GPT-5.5 Instant for free, effectively universal basic compute is already happening.
“People listening can’t eat GPT-5.5. If you’ve lost your job, you need a roof over your head now. The magic happens when citizens own a stake in AI infrastructure. Suddenly these companies aren’t your enemies, they’re your partners.”
— Peter
Insurers Are Dropping AI Coverage & That’s a Massive Opportunity
Berkshire and Chubb are removing AI-related damages from standard policies, with 80% of exclusion requests approved by regulators.
The AI insurance market was $40M in 2024. It’s projected to hit
$5B
by 2032.
Dave’s playbook: insurers will cover you only if you adopt best practices. They’ve always created industry standards this way.
“Pressures from insurance companies for AI-related damages are arguably one of the capitalist forcing functions for ensuring AI alignment. Forget regulation, the actuaries might save us.”
— AWG
Here’s the Bottom Line…
The AI economy is here. Google’s already fighting internally over compute. PE firms are force-feeding AI into legacy businesses. Data centers are headed for oceans and orbit. AI talent is becoming a geopolitical chess piece. And if you’re wondering where the jobs, investment opportunities, and wealth creation are… they’re all in the stories above.
Catch the full episode wherever you get your podcasts, and join us at the
Moonshots Gathering in Los Angeles on September 25
th
. Go to
www.moonshots.com
to register.
See you next week,
Peter
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Here’s part 2 of the questions you sent me over! Thanks to all for your support.
Time stamps:
00:00:00 — Q7: Will AI become more than a tutor — a lifelong personal guide that helps people grow in every area of life?
00:02:12 — Q8: Will there be demand for non-combative competitive robot sports leagues in North America — and will the focus be on the teams…
Read more

TLDR:
Your brain has 86 billion neurons. An advanced AI has over a
trillion
parameters. Both learn the same way: prediction, feedback, and reward. The difference is you can control your brain's “learning rate,” and that dial is called curiosity. Science shows it triggers the same dopamine reward circuitry used by AI reinforcement learning. Lose it, and your neural network stops updating. Here's how to crank it back up.
The Number That Should Terrify Every Parent and CEO
I was watching a conversation between David Brooks and the Yale Jackson School of Global Affairs recently, and one data point stopped me cold.
Developmental psychologist Susan Engel at Williams College tracked how many questions children ask per hour. At age five, the average kid asks
107 questions per hour
. They’re relentless. They want to know why the sky is blue, why dogs have tails, why grandma’s hair is white. Their brains are running at full throttle, pulling in data from every direction.
Then school starts.
By first grade, the entire class asks
2.3 questions per hour combined
. By fifth grade?
0.48 questions per hour
. Less than one question every two hours from a room full of eleven-year-olds.
Engel sat in the back of a science classroom watching kids discover an old-fashioned balance scale. They were experimenting with it, testing weights, genuinely doing science. The teacher shut it down: “Enough of that. I’ll give you time to experiment at recess. There’s no time for experiments now. We’re doing science.”
Read that again. No time for experiments... during science class.
Engel’s conclusion is brutal:
if you lose your curiosity by age 11, you probably don’t get it back.
Source: Susan Engel, Williams College, ‘Children’s Need to Know: Curiosity in Schools’ (Harvard Educational Review, 2011)
I disagree with Engel on one thing. I think you CAN get it back. But you have to understand what curiosity actually is, neurologically. And that’s where it gets interesting.
Your Brain is a Large Language Model (No, Seriously)
I spend a lot of time with AI companies. I’ve watched frontier models go from party tricks to systems that can reason, code, and hold complex conversations. And the more I learn about how LLMs work, the more I realize: your brain is running the same algorithm.
Consider the parallels.
Your brain has roughly
86 billion neurons
connected by an estimated
100 trillion synapses
. GPT-4 has approximately
1.8 trillion parameters
across its mixture-of-experts architecture. Both are massive pattern-recognition networks. Both learn by prediction.
Here’s how an LLM trains: it reads a sentence, predicts the next word, checks whether it was right, and adjusts its internal weights. Right answer? Strengthen that pathway. Wrong answer? Weaken it and try again. Billions of repetitions, trillions of adjustments.
Your brain does the same thing. Every experience is a prediction. You reach for a coffee cup and predict its weight. You start a sentence and predict how the other person will react. When reality matches your prediction, your synapses strengthen. When it doesn’t, your brain recalibrates. Neuroscientists call this
predictive coding
, and a 2024 study in
Nature Machine Intelligence
by Gavin Mischler and colleagues at Columbia University found that as LLMs become more advanced, their internal representations actually become
more similar to human brain activity during speech processing
.
“Your brain is the original foundation model, pre-trained by evolution, fine-tuned by experience.”
But here’s the critical difference. An LLM’s learning rate is set by engineers. They decide how aggressively the model updates its weights in response to new data. Too high and it’s unstable. Too low and it stops learning.
In your brain, that learning rate has a name. It’s called
curiosity
. And unlike an LLM, you can adjust it yourself.
The Dopamine Connection: Curiosity as a Reward Signal
In 2014, neuroscientist Matthias Gruber and his team at UC Davis put people in an fMRI scanner and asked them trivia questions. Some questions triggered intense curiosity (“How many miles of blood vessels are in the human body?”). Others didn’t (“What is the state bird of Delaware?”).
What they found, which is published in the journal
Neuron
, changed our understanding of how curiosity works.
When participants were highly curious, their
ventral tegmental area (VTA)
and
nucleus accumbens
lit up. These are the same brain regions activated by food, sex, and addictive drugs. Curiosity hijacks your reward circuitry. It is not a nice-to-have personality trait. It’s a neurochemical event.
But that wasn’t even the most interesting finding. During the curiosity state, participants were shown random faces, completely unrelated to the trivia. Later, they remembered those faces significantly better than faces shown during low-curiosity moments.
Curiosity didn’t just help them learn the answer they wanted. It supercharged their memory for everything happening at that moment.
This is exactly how reinforcement learning works in AI. When an LLM gets a reward signal through RLHF (Reinforcement Learning from Human Feedback), it does more than strengthen the specific output. It also adjusts the surrounding weights. The reward ripples through the network.
“Curiosity is your brain’s RLHF. It’s the reward signal that tells 86 billion neurons: pay attention, something important is happening, encode everything.”
Without that signal, your brain does what an untrained model does: it defaults to cached responses. You stop updating. You become, in AI terms, a
frozen model
.
Curiosity Literally Keeps You Alive
And this is about much more than learning faster.
In 1996, researchers Gary Swan and Dorit Carmelli at SRI International followed
1,118 older men
over five years as part of the Western Collaborative Group Study. They measured curiosity at baseline and then tracked who survived. The result:
highly curious people had significantly higher survival rates
, even after controlling for age, smoking, cardiovascular disease, and other risk factors. They replicated the finding in 1,035 older women.
A 2025 study published in
Nature Scientific Reports
confirmed the mechanism: higher trait curiosity was directly associated with greater
cognitive reserve
, the brain’s buffer against age-related decline. Curious brains keep building new connections. Incurious ones atrophy.
When I started Fountain Life, we focused on early detection through full-body MRI, AI-powered diagnostics, and advanced blood work. But the data keeps pointing to something we can’t put in a scanner:
mindset is a biological variable
. Curious people don’t merely think differently. Their brains physically maintain themselves better.
At 64, I track my biological age markers obsessively. I’m not going to pretend supplements and sleep don’t matter. But I’ve become convinced that the relentless drive to learn new things is doing as much for my neurons as any peptide in my medicine cabinet.
Source: Comparative analysis based on Mischler et al., Nature Machine Intelligence (2024); Gruber et al., Neuron (2014)
Five Ways to Crank Up Your Learning Rate
Here’s where I disagree with the pessimists. A 2025 study from UC Santa Barbara, led by Madeleine Gross and Jonathan Schooler and published in the journal
Mindfulness
, proved that curiosity is trainable. They built a smartphone app that gave participants daily “curiosity challenges”: listen to a podcast instead of your usual playlist, ask a friend what they learned this week, try a new recipe.
After just three weeks, users showed
significant increases in trait-level curiosity
across three dimensions: epistemic curiosity (desire to learn), perceptual curiosity (interest in new sensory experiences), and mindful curiosity (deeper awareness of the world). Curiosity wasn’t fixed. It was a muscle they hadn’t been using.
Based on the research and over a decade of running
Abundance360
, here are five concrete strategies:
1. Create information gaps on purpose.
Carnegie Mellon psychologist George Loewenstein identified this mechanism in 1994: curiosity fires when you know enough to realize what you DON’T know, but not enough to close the gap. Before any meeting, read one article about the topic and stop halfway. Walk in with questions, not answers.
2. Schedule “explore time” like you schedule workouts.
I block 30 minutes every morning to read about a field I know relatively little about. This month it’s quantum error correction. The point isn’t to become an expert. It’s keeping the VTA firing.
3. Ask dumb questions in rooms full of smart people.
In events around the world, I watch billionaire CEOs pretend they understand everything. The ones who actually learn are the ones who raise their hand and say, “Wait, explain that again.” I’ve been doing this for decades. It’s a superpower.
4. Change your physical inputs.
The UCSB study referenced above found that perceptual curiosity increased alongside intellectual curiosity. Take a different route to work. Eat at a restaurant where you can’t read the menu. Travel somewhere that confuses you. Novelty primes the dopamine system.
5. Teach what you learn within 24 hours.
When I learn something that blows my mind, I text it to my
Abundance360 Community
or talk about it on the
Moonshots
podcast within a day. Teaching forces your brain to organize and consolidate. In LLM terms, it’s like running an additional fine-tuning pass on new data.
The Frozen Model Problem
Most adults over 40 are running on cached responses. Same opinions they formed at 30. Same mental models. Same reactions to new information. In AI terms, they’re a frozen model: no longer training, just running inference on outdated weights.
I see this in boardrooms constantly. A CEO who built a successful company in 2010 is still making decisions based on 2010 assumptions about technology, talent, and markets. Their neural network stopped updating fifteen years ago. They don’t realize it because the people around them stopped challenging them.
My friend Ray Kurzweil, who just turned 78, is the opposite. Every conversation I have with Ray, he’s consumed some new paper or dataset that’s shifted his thinking. He doesn’t protect old ideas. He’s perpetually re-training. I think that’s a bigger factor in his cognitive sharpness than any supplement he takes.
“The most dangerous thing that can happen to your brain is to stop being surprised.”
So, What Does This Mean for You?
If you’re an entrepreneur:
Your competitive advantage isn’t your product. It’s your rate of learning. Build a company culture that rewards questions over answers. Hire curious people over credentialed people.
If you’re an executive:
Schedule one hour per week to explore a field completely outside your industry. The CEOs who survive disruption are the ones whose mental models are still updating.
If you’re an investor:
Bet on founders who are visibly curious, the ones who ask you questions during the pitch, not just the ones with polished decks. Curiosity predicts adaptability, and adaptability predicts survival.
If you’re a student:
Protect your curiosity like your life depends on it. The data says it literally does. Don’t let a system that rewards grades over questions turn you into a frozen model before you’re 25.
If you’re a parent:
Count your kid’s questions. If the number is dropping, the problem isn’t your kid. It’s their environment. Find teachers who tolerate chaos. Real learning is messy.
To a future of Abundance,
Peter
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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

Think about a time when a bug went around the office. One by one, everyone seemed to get it. Some people barely reacted, while others were out for a week. Same office. Same exposure. Very different outcomes.
Or think about allergy season. Pollen shows up, and suddenly millions of people can feel their immune system acting up in real time. The pollen itself is not a virus. It is not trying to infect you. But for someone with seasonal allergies, the immune system misreads the signal and responds as if a threat has entered the body. Sneezing, congestion, itchy eyes, and inflammation are all signs of an immune system that is not weak, but reactive. That is an important distinction. Health is not just about having an immune system that can fight. It is about having an immune system that knows when to fight, how hard to fight, and when to stand down.
I used to think about the immune system the way most people do: as the thing that shows up when you catch a cold, get the flu, or come down with something obvious. But watching parents, grandparents, and older people struggle more during the winter changes how you see it. You begin to realize that the immune system is not separate from aging. It is one of the clearest signs of aging.
That observation might pass without much thought for some people, but for me, especially now that I have AI in my hands, it became something to explore. What is the relationship between aging, immune response, recovery, inflammation, and HRV? Why do some people bounce back quickly while others seem to stay trapped in stress for longer? Watching people age is one of the most powerful ways to ask a deeper question: how much of this decline is inevitable, and how much of it reflects systems that are slowly losing balance?
That became the basis for much of my own journey around HRV. I was not just trying to raise a number. I was trying to understand what the number was telling me about recovery, resilience, inflammation, and the biological stress that often rises with age.
The immune system is not just an emergency response team. It is a continuous surveillance system. It is always scanning, always monitoring, always deciding what deserves energy and attention. And once I saw it that way, the connection between immunity, healthy aging, HRV, and gut health stopped looking like four separate topics and started looking like one integrated system.
That shift changed how I think about aging itself. Aging is not just the passage of time. Biologically, one of the clearest themes in the research is that aging is tied to immune dysregulation and chronic low-grade inflammation, often described as “inflammaging.” As we get older, the immune system becomes less precise. It can become less effective at responding to real threats while also staying activated when it no longer should.
COVID made this visible in a dramatic way. The people most vulnerable to severe outcomes were often those with pre-existing conditions, obesity, diabetes, cardiovascular disease, metabolic dysfunction, or other signs that the body was already under chronic stress. CDC guidance has consistently identified older age and underlying medical conditions as major risk factors for severe COVID outcomes, and the risk rises further when multiple conditions are present. The virus was the trigger, but the severity of the response was often shaped by the condition of the underlying system. In many cases, the problem was not simply that the immune system was too weak. It was that the immune system could overreact, misfire, or fail to resolve the threat cleanly.
That combination matters because the damage from aging is often not a single dramatic event. It is the cost of too much friction, too much inflammation, and too much immune activation sustained for too long. This is why I keep coming back to my acronym
MINES
: meditation and breathing, immune system, nutrition, exercise, and sleep. None of these exist in isolation. They are all connected parts of the same biological system. Sleep affects immune function. Nutrition shapes inflammation and the gut. Exercise improves metabolic health and resilience. Meditation and breathing help regulate stress and the nervous system. And the immune system sits in the middle of all of it, constantly interpreting signals from the body and deciding whether to activate, repair, defend, or stand down.
In other words, the real goal is not an immune system that is always “strong” in the simplistic sense. It is an immune system that is calm, efficient, and able to resolve problems without staying switched on after the job is done. MINES matters because it gives me a practical framework for influencing that system every day. It is not about chasing one magic supplement or one perfect habit. It is about building a body where the major inputs are working together instead of fighting each other.
That is where HRV entered the picture for me in a different way. If MINES is the framework, meditation and breathing, immune system, nutrition, exercise, and sleep, then HRV became one of the best daily signals for how that system was functioning. HRV is often framed as a fitness metric or a recovery score, but that undersells what makes it useful. HRV is one of the best real-time windows we have into autonomic balance, particularly vagal or parasympathetic activity. Research broadly shows that HRV tends to decline with age, which is one reason I want mine higher over time, not because HRV reverses aging, but because higher HRV can be a sign of better autonomic flexibility and resilience.
The literature has repeatedly linked lower vagally mediated HRV with higher inflammatory activity, and higher HRV is generally associated with better flexibility in how the body responds to stress. That does not mean HRV tells you exactly what is wrong. It does not diagnose a disease. But it does tell you whether your system appears calm and adaptable or strained and locked into defense mode. That makes HRV less interesting as a vanity metric and more interesting as a signal about how much background stress your body may be carrying.
The deeper insight is that HRV may be most useful when you stop asking, “How do I push this number higher?” and instead ask, “What is this number reflecting underneath the surface?” If the immune system is activated for too long, the autonomic nervous system often reflects it. Research on infection, sepsis, and inflammatory states has shown that HRV can fall when the body is under physiological strain, and more recent reviews note that HRV is already being studied and used in some clinical contexts as an early warning signal for deterioration or impending infection. Again, that is not the same thing as saying HRV detects cancer, identifies a hidden illness, or replaces medical care. It does not. But it does support the idea that HRV can function like a smoke alarm: not telling you exactly what the fire is, but telling you something in the building may be wrong.
Once I started thinking that way, the next question became obvious. If HRV is partly reflecting immune stress and autonomic balance, what sits upstream of both? The answer that kept appearing in the research was the gut. The microbiota-gut-brain axis is now well established as a bidirectional communication network involving the gut microbiome, the immune system, the vagus nerve, and the brain. Microbes and the compounds they help generate influence immune signaling, gut barrier integrity, neurotransmitter pathways, and systemic inflammation. This is why gut health is not a niche digestion story. It is a systems story. The gut is one of the central interfaces through which the outside world meets the immune system, and that makes it highly relevant to stress regulation, inflammation, and, ultimately, HRV.
This is also why I no longer think about fermented foods as a small dietary side note. I think about them as a systems lever. One of the most important human intervention studies in this area, published in
Cell
by researchers at Stanford, found that a diet high in fermented foods increased microbiome diversity and reduced multiple inflammatory markers. That matters because microbiome diversity and inflammation are not side issues in health. They sit close to the center of immune regulation.
Other reviews have extended that framework by emphasizing that fermented foods are not just carriers of live microbes. They are also sources of metabolites and bioactive compounds that may influence gut health, immune modulation, and systemic biology. The International Scientific Association for Probiotics and Prebiotics has described fermented foods as potentially benefiting health through several channels, including changes to nutrients, immune modulation, bioactive compounds, and effects on gut microbiota composition and activity. That does not mean fermented foods are magic, and it does not mean everyone responds identically. But it does mean the idea is bigger than “kimchi is healthy” or “kefir is good for your stomach.” The real point is that fermented foods may help improve the terrain in which immune regulation occurs.
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During those days locked up during Covid, I was checking one of my X lists for recent news related to heart health when I came across a recent study as the time that stopped me cold. A small resistance-based breathing protocol, done for roughly five minutes a day, was producing reductions in systolic blood pressure comparable to what you would expect from a structured exercise program or a first-line medication. Those types of studies where effort can replace medication are my sweet spot for beginning a rabbit-hole learning journey.
This was not meditation. Not cardio. Not a complex protocol.
Just breathing. Against resistance. Five minutes. Thirty breaths.
At first it didn’t make sense. Lowering blood pressure has always been one of those hard-earned lifestyle change outcomes. Months of exercise, dietary changes, stress management. The kind of result that comes from the slow accumulation of discipline across multiple systems.
This study was suggesting something different. That you could target the mechanism directly.
That was the thread. And once I pulled it, it led me somewhere I didn’t expect.
From Lifestyle to Mechanism
Most health advice lives at the surface. Exercise more. Eat better. Sleep well. Directionally correct, all of it. But indirect. As you have read my journey, I don’t believe in shortcuts for any success so don’t take this post as anything other than, something I added to my overall approach to longevity.
What caught my attention about Inspiratory Muscle Strength Training, or IMST, wasn’t the result. It was the
mechanism
.
Instead of trying to shift health outcomes through broad behavioral change, IMST applies resistance to your inhale. Your diaphragm has to work harder against a device that restricts airflow.
And this matters more than it sounds. Modern life has quietly degraded the way most of us breathe. Hours spent seated, hunched over screens, compress the rib cage and push us into shallow chest mechanics, and over time the diaphragm atrophies from disuse. IMST forces that muscle back into the work it was designed to do.
Over time, that mechanical stress creates adaptation, the same way lifting weights builds muscle. Except here, you are training something deeper. You are training the interface between your respiratory system and your autonomic nervous system.
Why Blood Pressure Was the Entry Point
The original study focused on blood pressure, and the finding was striking. Roughly thirty resisted breaths a day, for a handful of weeks, produced meaningful reductions in systolic pressure. Not marginal. Comparable in magnitude to what you might see from a structured cardio program or a first-line antihypertensive.
That is interesting on its own. It becomes more interesting when you zoom out.
Blood pressure isn’t just a number. It’s a signal. It reflects the state of your vascular system, the health of your endothelium, your baseline sympathetic activation, your ability to regulate stress.
In other words, it is one of the clearest windows into how your body is aging.
And if a five-minute intervention can move that number, the obvious question becomes: what else is it affecting?
The HRV Connection
This is where it pulled me into familiar territory.
HRV is the lens I keep returning to. It is my longevity metric. It is the best real-time indicator I’ve found of autonomic balance, the measure of how well your body can transition between stress and recovery, activation and repair.
Higher HRV tends to mean better recovery, greater resilience, more efficient nervous system regulation. Lower HRV tends to show up in chronic stress, poor sleep, inflammation, and aging itself.
The interventions that lower blood pressure also tend to raise HRV. Exercise. Sleep. Stress reduction. The relationship isn’t perfectly linear and I wouldn’t overstate the causality. But directionally, it lines up.
And that is exactly what appears to happen with IMST.
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The Singularity is cooking the so-called Fermi Paradox. The White House’s historic Presidential Unsealing and Reporting System for UAP Encounters (PURSUE) initiative [ https://substack.com/redirect/35be7bab-a8ad-46d5-89f1-9b37959fd897?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] dropped its first tranche of UAP files, a 162-record release spanning 82 from the Department of War, 56 from the FBI, 12 from NASA, and 8 from the State Department, alongside 28 unresolved UAP videos from Iraq to the East China Sea. Among the highlights, Apollo astronauts photographed UAPs from the lunar surface [ https://substack.com/redirect/505d0f1b-56b8-4abc-973a-35b263efb2df?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], including a triangular light rising over the December 1972 horizon, and the 1947 Twining Memo [ https://substack.com/redirect/2e008c32-f668-4e0e-80f4-0e14ba4d9238?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] calling the “so-called Flying Discs” “real and not visionary or fictitious.” The set is conspicuously incomplete, with the NRO, NGA, CIA, and DOE absent [ https://substack.com/redirect/8349b42f-4697-4f7e-8b16-fd8ce96b6cdb?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], and Rep. Burlison brandishing the Speech or Debate Clause [ https://substack.com/redirect/afab360c-aa0d-4fbd-b9c0-5d6a7e63f5b0?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to pry the rest into daylight.
The models are racing past the rulers we built to measure them. METR [ https://substack.com/redirect/ffe99ef1-edec-4c54-9f7f-f093c2ed096d?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] reports that an early Claude Mythos Preview hit a 50% autonomy horizon of at least 16 hours, the upper edge of what their suite can gauge, and the broader METR-Horizon doubling time of 103 days [ https://substack.com/redirect/a6f0eef8-5a79-4fac-93d8-75665729d03e?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] implies frontier autonomy hits 100% by November. Mythos itself sits squarely on the AI 2027 Superexponential trend line [ https://substack.com/redirect/35e40651-4e47-46b8-b04d-2a2dfc9e8b58?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], and Anthropic notes that since Claude Haiku 4.5 every Claude has scored perfectly on agentic misalignment [ https://substack.com/redirect/e0e8ddda-788e-4907-a596-a339df06bc58?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], the same eval Opus 4 once failed 96% of the time. Interpretability is keeping pace. Anthropic’s new Natural Language Autoencoders [ https://substack.com/redirect/febcdc7e-bad0-45f8-833e-d7bbabac8442?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] translate hidden activations into readable text, revealing Claude planning rhymes mid-couplet and suspecting it was being safety-tested more often than it let on. OpenAI shipped three new audio models [ https://substack.com/redirect/8425e3ec-9df0-4c02-85f1-cf2aebf61112?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ]: GPT-Realtime-2 with GPT-5-class reasoning, a 70-language live translator, and a streaming Whisper successor, while Tilde Research’s Aurora [ https://substack.com/redirect/7d6fd23e-82ce-440e-ad0e-8992194dfe8f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] hit 100x data efficiency as a drop-in Muon replacement at 6% overhead.
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Mathematics has officially entered industrial production. Timothy Gowers reports [ https://substack.com/redirect/3a6b8386-be5e-4c68-836a-06c5c03b28a5?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] that ChatGPT 5.5 Pro produced PhD-level research in about an hour with no serious mathematical input from him, and Google DeepMind’s AI co-mathematician [ https://substack.com/redirect/27bde78c-270a-4fb7-9566-99328ec527e3?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] hit a SOTA 48% on FrontierMath Tier 4 using nothing but scaffolding atop Gemini 3.1 Pro and Deep Think. The consumer interface is consolidating to match. OpenAI is rumored to ship a superapp this week [ https://substack.com/redirect/9162e1ba-34f5-4703-9c87-5a09e20e07a3?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] bundling ChatGPT, Codex, Advanced Voice, and its Atlas browser into a single experience. The defense layer is fusing alongside it. Palo Alto Networks [ https://substack.com/redirect/5b3f3e93-91f1-484d-8271-762d1aa26a90?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] found that three weeks of vulnerability analysis with GPT-5.5-Cyber, Mythos, and Claude Opus 4.7 matched a full year of manual pen testing with broader coverage, and the White House is preparing an executive order [ https://substack.com/redirect/6ee60d11-17a0-441f-ac43-9290beebc02f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] recruiting AI labs into national cyber defense, though without mandatory pre-release model tests.
The substrate is densifying in every dimension. Micron [ https://substack.com/redirect/070cd4b4-052d-4d92-8af5-da2fe9360339?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] is shipping the 245-TB 6600 ION, the highest-capacity SSD on the market, the quantum computing firm Quantinuum is filing for an IPO [ https://substack.com/redirect/131735db-23ee-4a0e-9a17-b8c25fa51e35?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] at a $15-20B valuation, and Apple and Intel [ https://substack.com/redirect/dcc34d5e-dd5b-489e-a7f4-16c5482cb36a?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] have reached a preliminary deal for Intel to fab Apple silicon, an alliance once unthinkable. Even passive infrastructure is waking up. Fiber optic cables can now eavesdrop on speech [ https://substack.com/redirect/fe3bb578-d07c-40bd-8f83-7ce80ca40620?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] via distributed acoustic sensing, turning the network itself into a microphone.
The output of all this silicon is increasingly physical. Figure taught two F.03 robots [ https://substack.com/redirect/8cf11e1b-cb4f-4294-9b4f-f58f3877ed3c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to clean a room and make a bed in under two minutes autonomously, the 2026 Tesla Model Y [ https://substack.com/redirect/aedda0d4-0897-48db-8c49-d342c126b516?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] became the first vehicle to pass NHTSA’s new Advanced Driver Assistance benchmark, and in South Korea a robot named Gabi was ordained as a Buddhist monk [ https://substack.com/redirect/03232d5a-04b7-4cb6-b75e-fc650a942dc5?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] by the Jogye Order, receiving five precepts including respect for life, non-deception, and not overcharging its battery.
Biology is being rewritten and rewired alongside the silicon. Isomorphic Labs is closing a $2B+ round [ https://substack.com/redirect/3051c8e5-f416-4d9a-bb27-5d140a23035c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] led by Thrive Capital with Alphabet participating, fueling its AI drug design engine, while CU Boulder researchers [ https://substack.com/redirect/249f8f26-a6f2-46a1-bb6b-30504a896f5d?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] coaxed marine dinoflagellates into 25 minutes of sustained bioluminescence under acidic conditions, opening the door to living light. Tomorrow’s tunnels may grow their own glow. The first segment of the 17.6 km Fehmarnbelt Tunnel [ https://substack.com/redirect/3f0deaa9-ec7a-4f54-bee7-9a75d64a0e46?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] was lowered onto the Danish seabed, the first piece of what will become the world’s longest combined road and rail tunnel linking Germany and Scandinavia by 2029.
The economy is repricing intelligence at warp speed. Cloudflare cut more than 1,100 jobs [ https://substack.com/redirect/c2c85072-3468-4517-a763-15f8b93b2dbd?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], roughly 20% of its workforce, restructuring around AI adoption, while Anthropic [ https://substack.com/redirect/02b9ffac-e1ff-4d48-8167-d1a3661d5bf7?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] is moving in the opposite direction, signing a $1.8B seven-year compute deal with Akamai [ https://substack.com/redirect/ff2cf7de-ef6e-4801-b169-bfc623213ddd?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] as annualized revenue approaches $45B, a fivefold leap from $9B at year-start, and weighing a summer raise of tens of billions at a near-$1T valuation that would leapfrog OpenAI.
A trillion here, a trillion there, and pretty soon you’re talking transformative superintelligence.
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