Earnings Report: LOW, TGT, VFC, TJX

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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?
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Introduction: Awareness Is the Foundation of Health
“You have power over your mind, not outside events. Realize this, and you will find strength.”— Marcus Aurelius, Meditations
One of the most important, and often overlooked, drivers of long term health is awareness. Not in the abstract sense, but in the ability to see patterns in how your body responds to the inputs of daily life. Sleep, nutrition, stress, alcohol, training, and travel are not isolated events. They are part of a system, and over time, they leave a measurable signature. This is where the Oura Ring has been so valuable for me. It does not simply track metrics. It reveals patterns. It shows how certain behaviors influence heart rate variability, or HRV, not just on a given day, but across multiple days and even weeks. The goal is not to control every variable or to maintain a perfect score. The goal is to recognize what consistently moves your system in the right direction, and what quietly pulls it away.
In that context, one of the most powerful insights is understanding that some of the most meaningful influences on HRV are not obvious. They are embedded in normal routines, which makes them easy to overlook. Travel is one of the clearest examples. It feels routine. It is often necessary. Most times, it is out of our control. But once you begin to see the pattern, it becomes clear that travel has a consistent influence on HRV, not only during the experience, but also in the recovery period that follows. This is the importance of solving for a problem using a systems thinking approach. Many times, especially with health, it is not just about what you are doing for you but how you are navigating stressors for work. Soon I will also go through the commute which was an eye opener for me.
There is also an important mindset embedded in this. Having access to this kind of data creates a choice. You can either engage with it and learn from it, or you can ignore it. Choosing not to measure or observe does not remove the impact. It simply removes the visibility. In that sense, opting out of awareness is, in itself, a decision. And over time, that decision compounds just as much as the behaviors being measured. I recognized that as I got older, all these little things add up if you want to focus on anti-aging.
Understanding HRV and the Recovery System
HRV reflects the balance between the parasympathetic nervous system, responsible for recovery and repair, and the sympathetic nervous system, which supports alertness and responsiveness. A higher HRV generally indicates a system that is adaptable and well recovered, while a lower HRV signals that the body is allocating more resources toward managing current demands.
Travel influences this balance in several ways at once. It changes sleep timing, introduces new environments, alters movement patterns, and often shifts nutrition and hydration. Together, these factors move the body toward a more activated state. The key is not the presence of activation itself. This is a normal and necessary part of life. The key is how efficiently the body returns to a recovered state afterward.
Why Travel Matters More Than We Realize
Travel is so embedded in modern life that it is rarely considered in the context of recovery. Flights, commutes, and trips are treated as routine logistics rather than physiological inputs. However, wearable data consistently shows that HRV tends to move lower during travel periods. More importantly, the effects often extend beyond the travel itself.
A single day of travel may be followed by multiple days where HRV gradually returns to baseline. During this period, sleep quality may still be adjusting, resting heart rate may remain slightly elevated, and overall readiness may not fully normalize. Over time, these periods can accumulate, shaping the broader trend of HRV across weeks and months.
Understanding this pattern is where awareness becomes valuable. Once you recognize that travel influences more than just the day it occurs, you can begin to plan around both the experience and the recovery window that follows.
Why the Effects Can Last After You Return
The extended influence of travel on HRV is driven by several overlapping factors. Circadian rhythm plays a central role. Even without crossing time zones, changes in sleep timing and light exposure can shift the body’s internal clock. When time zones are involved, this adjustment becomes more pronounced and may take several days to fully realign.
In addition, travel introduces a steady stream of inputs that require attention and adaptation. Navigating airports, adjusting to schedules, sitting for extended periods, and engaging with new environments all contribute. Each of these adds to the overall load the body processes. While none are extreme on their own, together they create a meaningful shift in how the system operates.
Behavioral patterns during travel also contribute. Meals may be less consistent, hydration can be overlooked, and alcohol is often more common. These factors influence sleep quality and recovery, which in turn affect HRV. Alcohol, in particular, tends to reduce overnight HRV and elevate resting heart rate, extending the time it takes for the body to return to baseline.
Why Aging Changes the Equation
As individuals get older, the body’s recovery processes become more deliberate. Baseline HRV may gradually decline, and the time required to adapt to changes, such as travel, can increase. What might have been a quick adjustment earlier in life can evolve into a more extended recovery period.
This does not mean travel becomes problematic. Rather, it highlights the importance of approaching it with intention. Managing the inputs around travel becomes a way to support the body’s ability to adapt and return to equilibrium efficiently.
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Think about a time when a bug went around the office. One by one, everyone seemed to get it. Some people barely reacted, while others were out for a week. Same office. Same exposure. Very different outcomes.
Or think about allergy season. Pollen shows up, and suddenly millions of people can feel their immune system acting up in real time. The pollen itself is not a virus. It is not trying to infect you. But for someone with seasonal allergies, the immune system misreads the signal and responds as if a threat has entered the body. Sneezing, congestion, itchy eyes, and inflammation are all signs of an immune system that is not weak, but reactive. That is an important distinction. Health is not just about having an immune system that can fight. It is about having an immune system that knows when to fight, how hard to fight, and when to stand down.
I used to think about the immune system the way most people do: as the thing that shows up when you catch a cold, get the flu, or come down with something obvious. But watching parents, grandparents, and older people struggle more during the winter changes how you see it. You begin to realize that the immune system is not separate from aging. It is one of the clearest signs of aging.
That observation might pass without much thought for some people, but for me, especially now that I have AI in my hands, it became something to explore. What is the relationship between aging, immune response, recovery, inflammation, and HRV? Why do some people bounce back quickly while others seem to stay trapped in stress for longer? Watching people age is one of the most powerful ways to ask a deeper question: how much of this decline is inevitable, and how much of it reflects systems that are slowly losing balance?
That became the basis for much of my own journey around HRV. I was not just trying to raise a number. I was trying to understand what the number was telling me about recovery, resilience, inflammation, and the biological stress that often rises with age.
The immune system is not just an emergency response team. It is a continuous surveillance system. It is always scanning, always monitoring, always deciding what deserves energy and attention. And once I saw it that way, the connection between immunity, healthy aging, HRV, and gut health stopped looking like four separate topics and started looking like one integrated system.
That shift changed how I think about aging itself. Aging is not just the passage of time. Biologically, one of the clearest themes in the research is that aging is tied to immune dysregulation and chronic low-grade inflammation, often described as “inflammaging.” As we get older, the immune system becomes less precise. It can become less effective at responding to real threats while also staying activated when it no longer should.
COVID made this visible in a dramatic way. The people most vulnerable to severe outcomes were often those with pre-existing conditions, obesity, diabetes, cardiovascular disease, metabolic dysfunction, or other signs that the body was already under chronic stress. CDC guidance has consistently identified older age and underlying medical conditions as major risk factors for severe COVID outcomes, and the risk rises further when multiple conditions are present. The virus was the trigger, but the severity of the response was often shaped by the condition of the underlying system. In many cases, the problem was not simply that the immune system was too weak. It was that the immune system could overreact, misfire, or fail to resolve the threat cleanly.
That combination matters because the damage from aging is often not a single dramatic event. It is the cost of too much friction, too much inflammation, and too much immune activation sustained for too long. This is why I keep coming back to my acronym
MINES
: meditation and breathing, immune system, nutrition, exercise, and sleep. None of these exist in isolation. They are all connected parts of the same biological system. Sleep affects immune function. Nutrition shapes inflammation and the gut. Exercise improves metabolic health and resilience. Meditation and breathing help regulate stress and the nervous system. And the immune system sits in the middle of all of it, constantly interpreting signals from the body and deciding whether to activate, repair, defend, or stand down.
In other words, the real goal is not an immune system that is always “strong” in the simplistic sense. It is an immune system that is calm, efficient, and able to resolve problems without staying switched on after the job is done. MINES matters because it gives me a practical framework for influencing that system every day. It is not about chasing one magic supplement or one perfect habit. It is about building a body where the major inputs are working together instead of fighting each other.
That is where HRV entered the picture for me in a different way. If MINES is the framework, meditation and breathing, immune system, nutrition, exercise, and sleep, then HRV became one of the best daily signals for how that system was functioning. HRV is often framed as a fitness metric or a recovery score, but that undersells what makes it useful. HRV is one of the best real-time windows we have into autonomic balance, particularly vagal or parasympathetic activity. Research broadly shows that HRV tends to decline with age, which is one reason I want mine higher over time, not because HRV reverses aging, but because higher HRV can be a sign of better autonomic flexibility and resilience.
The literature has repeatedly linked lower vagally mediated HRV with higher inflammatory activity, and higher HRV is generally associated with better flexibility in how the body responds to stress. That does not mean HRV tells you exactly what is wrong. It does not diagnose a disease. But it does tell you whether your system appears calm and adaptable or strained and locked into defense mode. That makes HRV less interesting as a vanity metric and more interesting as a signal about how much background stress your body may be carrying.
The deeper insight is that HRV may be most useful when you stop asking, “How do I push this number higher?” and instead ask, “What is this number reflecting underneath the surface?” If the immune system is activated for too long, the autonomic nervous system often reflects it. Research on infection, sepsis, and inflammatory states has shown that HRV can fall when the body is under physiological strain, and more recent reviews note that HRV is already being studied and used in some clinical contexts as an early warning signal for deterioration or impending infection. Again, that is not the same thing as saying HRV detects cancer, identifies a hidden illness, or replaces medical care. It does not. But it does support the idea that HRV can function like a smoke alarm: not telling you exactly what the fire is, but telling you something in the building may be wrong.
Once I started thinking that way, the next question became obvious. If HRV is partly reflecting immune stress and autonomic balance, what sits upstream of both? The answer that kept appearing in the research was the gut. The microbiota-gut-brain axis is now well established as a bidirectional communication network involving the gut microbiome, the immune system, the vagus nerve, and the brain. Microbes and the compounds they help generate influence immune signaling, gut barrier integrity, neurotransmitter pathways, and systemic inflammation. This is why gut health is not a niche digestion story. It is a systems story. The gut is one of the central interfaces through which the outside world meets the immune system, and that makes it highly relevant to stress regulation, inflammation, and, ultimately, HRV.
This is also why I no longer think about fermented foods as a small dietary side note. I think about them as a systems lever. One of the most important human intervention studies in this area, published in
Cell
by researchers at Stanford, found that a diet high in fermented foods increased microbiome diversity and reduced multiple inflammatory markers. That matters because microbiome diversity and inflammation are not side issues in health. They sit close to the center of immune regulation.
Other reviews have extended that framework by emphasizing that fermented foods are not just carriers of live microbes. They are also sources of metabolites and bioactive compounds that may influence gut health, immune modulation, and systemic biology. The International Scientific Association for Probiotics and Prebiotics has described fermented foods as potentially benefiting health through several channels, including changes to nutrients, immune modulation, bioactive compounds, and effects on gut microbiota composition and activity. That does not mean fermented foods are magic, and it does not mean everyone responds identically. But it does mean the idea is bigger than “kimchi is healthy” or “kefir is good for your stomach.” The real point is that fermented foods may help improve the terrain in which immune regulation occurs.
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### Bond yields soar, VVV leads altcoins, HYPE up 2x this year

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(truncated — read full post on source)

### Trump pauses Iran strike, SEC may allow tokenised stocks, semis fall

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(truncated — read full post on source)

The Singularity now has a release calendar, a futures curve, and an encyclical. At I/O, Google made Gemini 3.5 Flash [ https://substack.com/redirect/f5e962b4-5e46-4720-85d9-f0abef6b6d1f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] generally available, beating Gemini 3.1 Pro on Terminal-Bench (76.2%), GDPval (1656 Elo), MCP Atlas (83.6%), and CharXiv (84.2%) at roughly 4x the speed. Gemini Omni [ https://substack.com/redirect/735e812b-3e71-4538-9de9-c15770ab6995?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] collapsed text, image, audio, and video into one any-to-any model with SynthID baked in, while Pichai punted Gemini 3.5 Pro [ https://substack.com/redirect/5dc56c77-e363-4afe-8c26-7f5f964603b1?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], asking the audience to “Give us until next month to get it to you.” One observer [ https://substack.com/redirect/13ad5a54-73ed-4692-a35c-4e92bafda6d3?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] sniped that GPT-5.5-medium is already smarter and cheaper, suggesting “it might genuinely be over for anyone not named OpenAI or Anthropic.” The open frontier refuses to cede ground. Prime Intellect [ https://substack.com/redirect/b4d77a36-f5e4-4e41-b881-3be548e73318?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] released a 4,504-task training env that tripled small-model BFCL via self-play, Odyssey’s new Agora-1 [ https://substack.com/redirect/9011fa85-d93a-4d0a-b2aa-6ec424ebf475?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] lets four players share a GoldenEye-trained deathmatch as a learned game engine, and Cursor’s Composer 2.5 [ https://substack.com/redirect/932bd866-c578-4bbf-8341-7f9f3b073d6b?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] wraps Moonshot’s Kimi K2.5 with 25x more synthetic tasks, with xAI co-training a 10x successor on Colossus 2.
Science itself is being orchestrated. Gemini for Science [ https://substack.com/redirect/7168184f-34db-4fa9-aa2d-8641d891aa0c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] ties Co-Scientist, AlphaEvolve+ERA, and NotebookLM into one stack, with Nature papers and 100+ partners. Distribution is scaling accordingly. The Gemini app [ https://substack.com/redirect/382151f3-a741-48fa-b863-2c83aced4482?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] just crossed 900M monthly users, and Google is processing 3.2 quadrillion tokens per month [ https://substack.com/redirect/72459430-c94e-47bb-9e6a-c13786c4028e?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], up from 9.7T two years ago. The system tray itself is becoming agentic, with Android Halo [ https://substack.com/redirect/fa79e4d5-989d-42bc-8dcf-73c06b7f7b32?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] pulsing whenever Gemini agents are at work, fed by Antigravity 2.0 [ https://substack.com/redirect/9061c53d-05a9-4301-9b26-f4f2709bf875?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] (replacing Gemini CLI) and AI Studio’s one-prompt Android app builder [ https://substack.com/redirect/46b0680d-550e-4dc6-be27-eabf7aa6a66c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ]. On the world-modeling side, Project Genie [ https://substack.com/redirect/74ea7c7f-8cea-434e-a041-f3d0cec55ce2?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] can now reskin Street View as “Stone Age” or “Ocean World.” Search AI Mode [ https://substack.com/redirect/d6b7c4c4-6f1c-4e48-b54d-cd85a250e291?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] crossed 1B users on the biggest search-box upgrade in 25 years, while Universal Cart [ https://substack.com/redirect/be099b11-3988-4555-8489-d9bbe2c6b6bf?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] and the new AP2 protocol let Gemini Spark agents [ https://substack.com/redirect/cab3702b-1c18-4b0b-ae25-acd1c3537384?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] transact across merchants from Cloud VMs that run when devices are off. OpenAI’s verifier [ https://substack.com/redirect/54a2aa89-3d71-48d4-82ac-1c7f577d142f?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] adopts Google’s SynthID, the one layer where the rivals converge. Apple chose accessibility [ https://substack.com/redirect/93d3f654-b11b-4be8-98dc-ff898fd6d983?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], letting Vision Pro steer power wheelchairs by eye gaze. The flip side surfaced at IEEE, where imperceptible audio attacks [ https://substack.com/redirect/fe23435c-4f97-4a6e-92e2-170c9e02697e?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] hijacked 13 audio-LLMs at 79-96% success, while Amazon’s Alexa Podcasts [ https://substack.com/redirect/cd67799c-08f5-4d9f-ae41-5b7e207a9c1d?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] generates on-demand episodes from AP and 200+ outlets. Anthropic, reversing course, told Glasswing partners [ https://substack.com/redirect/477a20e4-d39e-412e-b910-868706c7b7fc?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] it “fully supports” them sharing Mythos cyber findings publicly.
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The substrate is straining. Intel [ https://substack.com/redirect/81aa0ae2-e724-4c6f-980c-163289d9f254?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] is muscling PC makers onto 18A while throttling Intel 7, and OpenAI’s new “Guaranteed Capacity” [ https://substack.com/redirect/e8a4385b-9326-41e5-a568-fc70c2faa20c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] tier sells multi-year compute lock-ins, with Altman warning the world “will be capacity-constrained for some time.” Microsoft shipped Azure Linux 4.0 [ https://substack.com/redirect/c34905f2-fd4f-4f54-91ab-a61f698c0c2c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], its first server distro, Armada raised $230M [ https://substack.com/redirect/e5615eac-8e1e-48bd-839a-731f4ad034f5?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] to mass-produce modular data centers, and Ornn [ https://substack.com/redirect/1caf485e-d349-48f9-8488-6f7c6fe9ca22?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] listed the first GPU compute futures on ICE. The capstone is Google and Blackstone’s $25B TPU JV [ https://substack.com/redirect/54f8b001-d159-4d8b-a52a-9c742d3f3971?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], targeting 500MW by 2027.
Atoms are following bits into autonomy. Figure’s F.03 [ https://substack.com/redirect/1377fdae-9ab4-4699-b9d0-0c77e9aea359?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] just cleared day seven of fully autonomous package sorting without failure, while the White House ballroom [ https://substack.com/redirect/18ce0590-6e7f-4776-817b-f9cc2d4c9841?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] was unveiled with a “drone-proof” steel roof that doubles as a drone port. Google’s Android XR audio glasses [ https://substack.com/redirect/5009e3fe-fa37-43ef-b9ec-4d985d80d0e8?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] ship this fall, putting Gemini one tap from the temple. Higher up, SpaceX will buy Cursor [ https://substack.com/redirect/1e748ad0-d415-476e-a72a-7e7dfc7a1f65?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] ~30 days after its June IPO, exercising its $60B option, and Astrolight [ https://substack.com/redirect/d8250367-7bf1-4f15-9c65-6b5f6aa3a441?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] opened a new ESA-backed CubeSat laser-link station in Greece. Above it all, the next PURSUE UAP drop [ https://substack.com/redirect/eb9d26d7-cf6e-4c3e-b94f-df25bcd2f744?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] is imminent after the last release pulled 1B views, prompting Musk’s deadpan follow-up [ https://substack.com/redirect/e913becd-40d8-4236-9bac-3ee704c13425?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], “Where are there aliens?”
The economy is rerouting around the agent stack. Standard Chartered [ https://substack.com/redirect/6609d819-114b-4879-bb26-8da6f2ea09c0?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] is shedding 7,000 jobs, with CEO Bill Winters calling it “replacing in some cases lower-value human capital,” while Demis Hassabis [ https://substack.com/redirect/5fa03563-a408-4094-a689-f8980577ca68?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] urged firms to use AI gains to do more rather than fire people. Minnesota became the first state to criminalize hosting Polymarket and Kalshi [ https://substack.com/redirect/d4b263a9-6684-4728-ad74-145299894ed8?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] (CFTC sued same-day) and to ban nudification apps [ https://substack.com/redirect/12ff0c8a-c896-46cf-bcc0-cd24fa573dd4?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] at $500k per violation. Meta is cutting 10% of staff [ https://substack.com/redirect/4934a41a-0fe4-4284-a9a6-50a840bc7695?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] while reassigning 7,000 into “AI native” reorgs. Karpathy joined Anthropic [ https://substack.com/redirect/f1e31c82-03d5-4dd7-8ee9-6f5c84fc1208?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] under Nick Joseph to lead pre-training (essentially training Claude to accelerate Claude), while OpenAI’s Noam Brown reframed the hire [ https://substack.com/redirect/24ed0107-a6ad-4466-8806-58c273a2114b?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] as frontier labs “collectively advancing the most important tech of our era.” Polymarket and Nasdaq Private Market [ https://substack.com/redirect/6d051e6d-e503-40b0-ad62-d67cd5cd09b3?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] priced Anthropic at 93% to cross $1T this year and 69% to IPO before OpenAI, and Hassabis himself was outed [ https://substack.com/redirect/0fd74570-a1a8-42b8-b886-359f6cea5494?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] as an early Anthropic angel, the moralist quietly long Anthropic. In cyberpunk mode, the FBI is shopping for nationwide license-plate-reader access [ https://substack.com/redirect/705257ab-9ff0-47ef-909c-a79e5d0d2581?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], and Iran launched Hormuz Safe [ https://substack.com/redirect/c55f5020-43d3-4fe9-bf08-6505c9d20ef3?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], Bitcoin-backed “shipping insurance” for the Strait of Hormuz, projected to rake in $10 billion. To bless the whole stack, Pope Leo XIV [ https://substack.com/redirect/bd5e4dca-f679-4309-b789-47cd1029f2ba?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] releases his first encyclical, “Magnifica humanitas,” on May 25 alongside Anthropic co-founder and interpretability lead Christopher Olah.
And the Word became weights, and dwelt among us.
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The Singularity has had a price since March, but no major exchange to trade it on, until now.
Two months ago, I wrote [ https://substack.com/redirect/674dc76e-6bf7-438b-89d6-cfcf10a2dcf7?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] about the moment compute became a tradable asset class. Ornn [ https://substack.com/redirect/2ccc496d-a7db-46f7-9dfe-30ee369d604a?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], a company I advise and helped form with backing from 021T Capital [ https://substack.com/redirect/9d604020-750f-41d6-b1f8-33ee14bd6a7c?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], began publishing the Ornn Compute Price Index (OCPI) on the Bloomberg Terminal [ https://substack.com/redirect/ee34c363-fc61-41c0-9ecb-202e0b738679?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], the first compute benchmark that derivatives can reference and settle against. OCPI settles against cleared GPU prices, not rate cards or surveys. That was the credentialing step. Bloomberg distribution is how a commodity announces to institutional capital that it is ready. But credentialing is not clearing. To finish the arc oil walked in the 1980s and natural gas in the 1990s, compute had to find a home at a major regulated derivatives exchange. Today it does.
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Ornn [ https://substack.com/redirect/2ccc496d-a7db-46f7-9dfe-30ee369d604a?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] plans to launch exchange-listed futures on GPU compute through Intercontinental Exchange [ https://substack.com/redirect/06f97727-e62f-4c90-98fa-d2320024f0cd?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] (ICE), parent of the New York Stock Exchange and operator of one of the world’s leading networks of regulated exchanges and clearing houses. The contracts will be U.S. dollar denominated and cash-settled, and will reference the OCPI series covering H100, H200, B200, RTX 5090, and additional GPU types. They will launch pending regulatory approval.
ICE was founded by Jeff Sprecher [ https://substack.com/redirect/809afd15-9c3d-4f5d-af8d-75138ece0214?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in 2000, three years after his 1997 acquisition of Continental Power Exchange, a struggling Atlanta trading platform, for $1,000 from MidAmerican Energy [ https://substack.com/redirect/6a476372-366d-4e0f-a708-8512f8a22df5?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] (later acquired by Warren Buffett’s Berkshire Hathaway). The vision was to drag opaque OTC energy markets into transparent, electronically cleared trading. The parallel to GPU compute today is exact. ICE acquired London’s International Petroleum Exchange [ https://substack.com/redirect/221e59cb-cf73-4bb4-9ce1-b22f61d57056?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in 2001, inheriting the Brent crude futures contract [ https://substack.com/redirect/06fc62ae-6b2c-47f6-9096-6a1bb86d6a95?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] IPE had launched in June 1988, and grew Brent into the price reference for roughly three-quarters of the world’s traded oil. ICE went public on the New York Stock Exchange in 2005, acquired the New York Board of Trade [ https://substack.com/redirect/9f91a58f-a650-4b6e-bdab-22561711a4f0?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in 2007, built ICE Clear Europe [ https://substack.com/redirect/000985ee-bab9-41b0-9f80-3f02b7e16276?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in 2008 (the first new major UK clearing house in over a century), and then acquired [ https://substack.com/redirect/ea02ee97-7f00-4cf5-8a5a-fd9d5a4b5884?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] the NYSE itself in 2013. The same plumbing that runs Brent crude, TTF [ https://substack.com/redirect/8071bdcf-869c-4dd9-a781-d1a7fcb29041?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] natural gas, EU carbon allowances, and the world’s benchmark sugar, coffee, and cotton contracts (the last of which [ https://substack.com/redirect/43d57fb6-85f6-4d3e-b6b2-7188db5182de?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] has traded continuously in New York since 1870) is now being extended to compute.
Once OCPI-referenced futures clear at ICE, lenders financing GPU buildouts can hedge their exposure on the same infrastructure that clears oil and gas. Insurers can underwrite residual value risk against a regulated curve. Hyperscaler treasury desks can lock in forward compute costs the way airlines have locked in jet fuel since the 1980s. Sovereign infrastructure funds and pension capital, pools that cannot touch unregulated venues at meaningful size, can finally participate in the $7 trillion [ https://substack.com/redirect/b8cf5dcb-3d55-45ec-baef-5be0d2b89d49?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] compute buildout with the same risk machinery they use everywhere else. The index was the foundation. The exchange is the keystone. The arch can now bear weight.
As Peter Diamandis and I argued in Solve Everything [ https://substack.com/redirect/0e145dd3-037e-4dba-b9cc-368d9d422ea3?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], the Intelligence Revolution turns every scarce domain it touches into an abundant one, but only after the financial infrastructure catches up. Edison [ https://substack.com/redirect/1b8cd6ee-4ecd-4a67-8fa4-0835977979b8?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] built the generators; Samuel Insull [ https://substack.com/redirect/89ae734b-ca08-4320-baf0-7e544c677b19?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] made them financeable through Commonwealth Edison and the modern utility holding company. Carnegie [ https://substack.com/redirect/92515c44-bf5b-4c5a-a3b4-6481fcd42c24?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] made the steel; J.P. Morgan [ https://substack.com/redirect/d198d958-b68d-43cb-9f6c-0652e9fe04d7?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] made it bankable through the 1901 merger that formed U.S. Steel [ https://substack.com/redirect/9e275f7b-8aa5-44ba-9c91-f53e1dd67fc6?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ], the world’s first billion-dollar corporation. Drillers found the shale; futures curves [ https://substack.com/redirect/cc1b91b2-55dc-47b0-b48e-709d62baf24e?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] let the capital follow. Every commodity that powered a phase of civilization eventually traded next to the others on the same plumbing. Oil took over a century, from Drake’s well [ https://substack.com/redirect/7f6570c7-05d9-47f9-997a-790809cd8f70?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in Titusville in 1859 to WTI [ https://substack.com/redirect/28fc77dd-d2b8-47e6-aef0-a04a89bdf8c9?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] futures on NYMEX [ https://substack.com/redirect/a92a5a1e-5e0f-420c-8066-140cb3a763ca?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in 1983. Gas took even longer, from America’s first gas works [ https://substack.com/redirect/9032cf80-2aa5-44eb-97d8-0ce14eab7fce?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] in Baltimore in 1816 to Henry Hub [ https://substack.com/redirect/daea5c69-be9c-4ad3-bb6e-8d653b397719?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] futures in 1990. Compute took only years because oil and gas had already built the machinery. Those years end now.
The Singularity used to be an asset you could own only through equities, the way investors got oil exposure for the seven decades between the 1911 Standard Oil breakup [ https://substack.com/redirect/c2e79eb4-a8b0-4642-9140-73af1dd4e044?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ] and the launch of crude futures. That era ends when the commodity gets its own exchange-listed contract. For compute, that day is today.
Those interested in Ornn’s compute pricing and financial products can learn more at ornn.com [ https://substack.com/redirect/2ccc496d-a7db-46f7-9dfe-30ee369d604a?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ].
(This post is for informational purposes only and does not constitute investment, financial, or trading advice. Nothing herein is a recommendation to buy, sell, or enter into any transaction involving GPU compute, derivatives, or any other financial instrument. Statements about future capabilities and product listings are forward-looking, subject to regulatory approval, and subject to uncertainty. I have a financial interest in Ornn [ https://substack.com/redirect/2ccc496d-a7db-46f7-9dfe-30ee369d604a?j=eyJ1IjoiODI5Z29vIn0.G3cZ5_j7JDh0OezT7WoRk_oWWFtesplUbtYpvMNHv8c ].)
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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

Five years ago, if you had a great idea you needed an engineering team, a seed round, and more than 18 months to find out if it worked.
That era is over.
The cost of shipping software has dropped 10x in 24 months. A solo founder can build in a weekend what used to require a team and a year. The ‘builders class’ known as anyone with literacy, internet access, and a frontier AI model, is rapidly expanding from 47 million developers today toward a projected 1 billion by 2030.
This is the fastest expansion of a participating population since the smartphone. Before that, the printing press. Before that, the alphabet.
Every time the builder class expands by an order of magnitude, civilization changes. The question is: what direction does it change in?
That’s the reason I just launched the
“
Build with Gemini XPRIZE”
— a $2 million global hackathon, built on Google’s Gemini stack, to find the builders who will point these tools at problems that actually matter.
Let me tell you why.
High Agency Is the New Credential
A 41-year-old self-taught coder in LA named Matthew Gallagher launched a GLP-1 weight-loss telehealth platform in September 2024. He spent $20,000. An AI chatbot to handle customer service.
His team consists of two people: him and his brother.
In his first year revenue crushed $401 million with a total of 250,000 customers. His current run rate: over $3 million per day, tracking toward $1.8 billion in 2026.
I want you to think about that for a second. Two people. $20,000. A dozen AI tools wired into an existing supply chain. No venture round. No engineering team.
That is what happens when high agency meets a real, painful problem: the cost of building drops to nearly zero.
And it’s why we put $2 million on the line with Google and XPRIZE to inspire more people like him. To inspire people build companies rather than find jobs.
The Proof Is Already Here
I’m not being provocative for the sake of it. The data is clear.
A Harvard-linked study found that roles heavy on repetitive, structured tasks, the kind where your value was being “the person who does X”, saw job postings drop roughly 13%. Meanwhile, AI-augmented roles requiring analysis, judgment, and creativity grew about 20%.
Read that again. The market is quietly deleting jobs where your value was doing the task and replacing them with jobs where your value is figure out how to plug AI into the opportunity and own the outcome.
McKinsey data shows job ads asking for “AI fluency” have surged faster than almost any other skill category. Not AI research. Not machine learning PhDs, but fluency. The ability to pick up these tools and do something useful with them.
The old question was: What degree did you get? What job can you land? Where did you go to school?
The new question is simpler: What will you build?
I’ve been saying for years that the world’s biggest problems are the world’s biggest business opportunities. That hasn’t changed, what’s changed is who gets to go after them. It used to be credentialed engineers with millions in backing. Now it’s anyone with agency, passion and a laptop.
That is a civilizational shift. And most people are sleeping through it.
Not convinced? Let me tell you another story that’s equally as shocking:
In December 2024, an Australian tech founder named Paul Conyngham used AI, including Google DeepMind’s AlphaFold to design a custom mRNA cancer vaccine for his dog. He’s not a molecular biologist. He’s a guy with a laptop and a dog he loves very much. Eight weeks later, his dog’s largest tumor had shrunk by 75%.
In 2025, a 26-year-old solo founder named Maor Shlomo sold his AI-coded startup Base44 to Wix for $80 million cash. Built alone. Six months. 300,000 users. $3.5 million in annual revenue. No employees and no investors.
Stripe’s 2024 data says 44% of profitable software businesses are now run by solo founders.
I’ve been writing about exponential technologies for thirty years and I have never seen a cost curve drop this fast.
The builders are arriving by the millions. The only remaining question is what will you build?
The Vibe Coding Revolution
Andrej Karpathy gave it a name in February 2025: vibe coding. Software built by describing what you want in plain English. Now its default mode for a new generation of builders.
Let me give you some examples that would have been impossible three years ago.
A writer built Taste, a social network for sharing food preferences and dietary needs. She didn’t wait to “learn to code.” She vibe-coded a social app because her friends needed it.
A recruiter built a resume-scoring agent using Zapier and natural language. It reads job descriptions, scores incoming resumes, and plugs into thousands of apps.
These examples are just some of the millions occurring across the globe and companies are starting to notice.
For reference, here’s the cost picture: inference costs have fallen over 90% in a single year. Open-source models are running on laptops and companies are using AI to generate their own training data, cutting labeling costs by another 90%. Whole categories of “we should just buy this SaaS” are turning into “let’s have one person vibe-code it this weekend.”
Every time the builder class expands by an order of magnitude, civilization changes. We are in the middle of one of those expansions right now.
The $50 Trillion Question
Here’s something that keeps me motivated to do what I do:
Half of global GDP runs through five sectors:
Education. Human potential. Entrepreneurship and small business. Financial access. Professional services.
The worst part is these sectors are running $50 trillion inefficiently. Systems designed in the 1970s, running on processes that haven’t been meaningfully updated since the fax machine. A teacher in Nairobi with a curriculum that works but no platform to scale it. A nurse in Manila who knows exactly how to fix hospital scheduling but has never written a line of code. A farmer in Iowa who could save her entire industry if someone would just build the software she’s been describing for a decade.
Those people can build now. Gemini doesn’t care about your résumé, your zip code, or your CS degree. It cares about your problem description.
The question is whether we point these tools at the world most important problems, I’m betting we can, and we’re putting real money behind it.
The “$2M” Build with Gemini XPRIZE: How It Works
The mandate is simple:
Solve a real problem that impacts 100,000 people or more. Generate real revenue doing it.
We don’t care about your pitch deck. We care about lives changed and problems solved.
The prizes:
$2 million total. $500k grand prize. No equity taken. No clawbacks. Cash wires to the winner.
The timeline:
90 days. Submissions close mid August. The finale is September 25 in downtown Los Angeles at the Moonshots Gathering (moonshots.com).
If you’ve been sitting on an idea for ten years, the wait is over. You can build it now for $20/month and a weekend.
If you’re a domain expert in a field software hasn’t touched, you have 90 days to rewrite the rules of your industry. Nobody knows your industry’s problems better than you, and for the first time in history, that knowledge is enough.
Where I Stand
I’ll be direct about why this matters to me personally.
I’ve spent my career building XPRIZE on a single principle: incentive prizes break problems that committees cannot. $10 million became a private spaceflight. $15 million became adaptive learning for kids without schools. $100 million became carbon removal at scale.
But this one is different.
Every previous XPRIZE required specialized teams with deep technical credentials. That was the whole point, you needed rocket scientists to build rockets.
This is the first XPRIZE where the winner might be someone with no technical background at all. A teacher. A nurse. A farmer. Someone who has spent twenty years understanding a problem and now, for the first time, has the tools to solve it themselves.
That’s not a feel-good story. That’s a structural shift in who gets to participate in building the future. And I believe it will produce solutions the credentialed class never would have found, because they never lived the problems.
The world’s biggest problems are the world’s biggest business opportunities. If you solve a problem for a billion people, you become a billionaire. It’s that simple.
A laptop. Wi-Fi. One weekend. That’s the entire barrier between you and what comes next.
Learn more about the Build With Gemini XPRIZE
To an Abundant future,
— Peter

TLDR:
In just 100 days, Anthropic has unhobbled Claude three times: design tools in April (Figma dropped 7%), legal tools in February and again this week (Thomson Reuters fell as much as 18%, RELX dropped 14%), and small business tools on May 13 (targeting QuickBooks, PayPal, HubSpot, and Canva). Anthropic’s revenue has exploded from $9 billion to $30 billion run rate in roughly four months. This is a systematic dismantling of the $376 billion SaaS economy, one vertical at a time. If you’re an entrepreneur, this piece ends with six moats that will determine whether your company survives or gets dissolved.
In the past month, Claude has disrupted design, legal, and small business SaaS companies. I call this the “unhobbling” of latent AI capability. Claude could always do design, draft legal documents, analyze contracts, manage workflows, and help run a small business. But until now, those abilities were buried inside a general-purpose chatbot.
In the past few weeks, Anthropic changed that. It packaged Claude’s capabilities into explicit, user-friendly vertical tools. Some will call that productization. I call it a direct frontal assault on vertical SaaS revenue.
THE UNHOBBLING ACCELERATES
In late April, Claude Design dropped and Figma’s stock sank 7% in a single afternoon. Adobe fell roughly 2%. I made a prediction: legal was next.
Then on Monday, May 12, Anthropic doubled down on
disrupting legal services
. They released more than 20 new MCP connectors linking Claude to the software law firms actually use: Box, Everlaw, DocuSign, Microsoft 365. Plus 12 practice-area-specific plugins covering everything from M&A due diligence to employment handbook drafting, according to LawNext and Fortune. When Claude first moved into legal back in February, the market reaction was immediate and violent: Thomson Reuters shares plunged as much as 18%, RELX (LexisNexis’s parent company) fell 14%, and LegalZoom cratered, according to Complex Discovery and Business Insider.
A day later, on May 13,
Claude for Small Business launched
. This one is personal for me because I talk to entrepreneurs every day who are building in this space.
Here’s what Anthropic did: they took the tools that small businesses depend on — Intuit QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365 — and wired Claude directly into them. Fifteen ready-to-run workflows covering finance, operations, sales, marketing, HR, and customer service.
As Daniela Amodei, Anthropic’s president, put it: “Small businesses make up nearly half the American economy, but they’ve never had the resources of bigger companies. AI is the first technology that can finally close that gap.”
She’s right. And that should terrify every SaaS founder selling tools to small businesses.
What’s next?
By December, expect Claude for Healthcare to gut the HER-and-billing complex, Claude for Real Estate to obsolete the MLS-CRM-transaction-management stack, and Claude for Construction to collapse the project-management, takeoff, and bidding tools — three industries where the software is universally hated, the workflows are templated, and the incumbents have spent a decade charging rent on PDFs.
THE PATTERN
I’ve watched this pattern play out my entire career. I even built a framework for it: the 6Ds of Exponentials — digitize, deceptive, disruptive, dematerialize, demonetize, and democratize. What Anthropic is doing right now hits all six.
The point people keep missing: Claude could already do most of this. The model had these capabilities. Anthropic just removed the guardrails, optimized the interfaces, and built the connectors. That’s unhobbling. Not new technology — the removal of restrictions on latent capabilities.
Think about what that means. Every frontier model (Claude, GPT, Gemini) likely has latent capabilities that haven’t been packaged yet. Every vertical SaaS company is sitting on top of capabilities that already exist inside these models, waiting to be unwrapped. The only question for any given vertical is: when does the model provider decide to unwrap it?
HISTORY REPEATING ITSELF
In the early 2000s, I watched Kodak get obliterated by digital photography. They had 145,000 employees and a $30 billion market cap at their peak in the late 1990s. Instagram had 13 employees when Facebook bought it for $1 billion. The camera didn’t disappear. It dematerialized into your phone. Kodak’s entire business model was a scaffold around a technology that got subsumed.
I told this story at Singularity University for years. Most audiences nodded politely and assumed it wouldn’t happen to their industry. Now I tell it at my Abundance Summit, and the room goes quiet. Because every founder in the audience can see it coming for them.
The $376 billion global SaaS market, projected to hit $1.5 trillion by 2034 according to Fortune Business Insights, is built on one assumption: that vertical software companies will keep owning their niches. Unhobbling demolishes that assumption.
And the speed is what’s different this time. Kodak had a decade of warning. Thomson Reuters got a single Monday morning.
THE SCORECARD SO FAR
Let me lay out what’s actually happened in the past 100 days:
Design (April 17): Claude Design launched. Figma dropped 7%. Adobe fell ~2%. Anyone who can type a sentence can now design a website, a prototype, or a pitch deck.
Legal (February 3, expanded May 12):
Claude Legal launched with document review, case law research, deposition prep. Thomson Reuters fell as much as 18%. RELX dropped 14%. This week’s expansion added 20+ connectors and 12 practice-area plugins. Every associate at every law firm already uses AI. The plugins just made it official.
Small Business (May 13):
Claude for Small Business wired into QuickBooks, PayPal, HubSpot, Canva, DocuSign. Fifteen workflows for payroll, invoicing, marketing, HR. Intuit was already the worst-performing stock in the S&P 500 in early 2026, per Fortune. This announcement will not help.
Revenue acceleration
: Anthropic hit $87 million run rate in January 2024. By December 2024: $1 billion. End of 2025: $9 billion. February 2026: $14 billion. March: $19 billion. April 2026: $30 billion (per VentureBeat). That trajectory tells you everything you need to know about where the market is heading.
I now run an exercise at every Abundance Summit. I ask the room: name a SaaS tool you pay for that a frontier model couldn’t replicate in twelve months. The silence gets longer every quarter.
THE 6Ds IN REAL TIME
Digitized
: Design, legal research, financial modeling, small business operations — all now reducible to tokens.
Deceptive
: Six months ago, Claude’s design capabilities seemed like a toy. Nobody at Figma was losing sleep. Claude’s legal skills seemed like a demo. Nobody at Thomson Reuters was panicking.
Disruptive
: February 3. April 17. May 12. May 13. Four dates. Billions in market cap erased.
Dematerialized
: The design tool, the legal research platform, the small business operations suite — all disappeared into the AI. No separate app required.
Demonetized
: What cost $20/month per seat (Figma), $24,000/year per seat (Bloomberg Terminal), or hundreds per hour (legal research) is now included in a Claude subscription.
Democratized
: Anyone who can type a sentence can now design a website, review a contract, close their books, or run a marketing campaign. No training. No expertise. No Figma account.
This cycle used to take a decade. Now it plays out in an afternoon.
THE ENTREPRENEUR’S SURVIVAL GUIDE: SIX MOATS AGAINST UNHOBBLING
If you’re building a company right now, this is the section that matters.
A scaffold is a thin software layer wrapped around a language model. It adds a UI, some prompt engineering, maybe fine-tuning. And it’s one unhobbling from worthless.
A business has moats the model can’t replicate. Here are the six that matter:
1. Deep customer relationships:
Box and Dropbox survived the onslaught of Google Drive and iCloud. Their stock performance hasn’t been pretty — both have struggled relative to their IPO prices — but they’re alive because they were obsessively focused on enterprise customers in ways the big platforms never bothered to be. The lesson: customer lock-in may be the only thing standing between you and annihilation. Know your customers by name. Embed yourself in their organizations. Make switching painful not through contracts but through indispensability.
2. Proprietary data that creates a flywheel:
This is what Intuit’s CEO Sasan Goodarzi is betting the company on. He calls data QuickBooks’s “durable advantage,” per VentureBeat. Forty years of small business financial data. A hundred million customers. Claude can do bookkeeping, but it can’t replicate the dataset that makes QuickBooks predictions accurate for a specific industry, region, or business type. The formula: more users generate more data, better data improves the product, a better product attracts more users. If you’re not building a data flywheel, you’re building a scaffold.
3. A powerful, trusted brand:
When your accountant sends you a tax return, you want to see “Intuit TurboTax” on it, not “Claude did this.” When a lawyer files a brief, the client wants to know a human with a bar number reviewed it. Brand is a trust proxy. In regulated industries especially, brand carries weight that no AI can replicate overnight. But brand alone won’t save you. It buys you time. Use that time to build the other five moats.
4. Physical-world integration:
AI is extraordinary at processing information. It’s much less good at interacting with the physical world. If your business involves sensors, robots, logistics, hardware, or anything that requires atoms to move, you have a moat that’s structurally harder to unhobble. A fulfillment network, a fleet of delivery vehicles, a network of physical clinics like my Fountain Life centers, a manufacturing operation — these are moats that can’t be dissolved with a software update.
5. Regulatory expertise and compliance moats:
Healthcare (HIPAA), finance (SEC/FINRA), defense (ITAR), pharmaceuticals (FDA). The companies that have spent years building compliance infrastructure, earning certifications, and developing relationships with regulators have something that can’t be replicated by an AI model, no matter how capable. Epic Systems charges hospitals billions not because their software is good (ask any doctor), but because their compliance, certification, and integration infrastructure took decades to build.
6. Network effects and ecosystem lock-in:
Nvidia’s CUDA ecosystem has over 4 million developers. Tesla’s FSD fleet has accumulated over 10 billion miles of real-world driving data. Salesforce has an ecosystem of thousands of integrators and consultants. The more participants in your network, the more valuable the network becomes, and the harder it is for anyone — including an AI model provider — to replace you. Build platforms, not products. Create ecosystems where other businesses depend on your existence.
The meta-principle: if you can describe your entire value proposition in a prompt, you’re a scaffold. If dismantling your business requires physically deconstructing relationships, moving atoms, violating regulations, or unwinding network effects, you’re a business.
WHAT THIS MEANS FOR YOU
If you’re an entrepreneur:
Stress-test your company right now. Which of the six moats do you have? If the answer is zero or one, you’re in trouble. Build model-agnostic from day one. Your AI provider is a commodity. Today it’s Claude. Tomorrow it’s GPT-5.5 or Gemini. The model is a utility, like electricity. You don’t build a competitive advantage on which power company you use.
If you’re an executive:
Audit every SaaS tool your company pays for. Ask: could a frontier model do this natively within 12 months? If yes, renegotiate or exit the contract now.
If you’re an investor:
For each SaaS holding in your portfolio, ask one question: is this a scaffold or a business? Does the value live in the model’s capabilities or in something the model can’t replicate? Anthropic went from $9 billion to $30 billion run-rate in four months. That revenue came from somewhere.
If you’re a student:
Don’t optimize for tools that are being dissolved. Learn to think, not to operate software. Judgment, creativity, and the ability to direct AI are the durable skills. Nobody will ever pay you to use Figma. They’ll pay you to know what to build.
I’ve spent thirty years studying how exponential technologies disrupt established industries. The pattern is always the same: the incumbents see it coming and still can’t move fast enough.
Every SaaS company on earth just got put on notice. The capabilities are already inside the models. The only question is when the guardrails come off.
Anthropic has made it clear: they’re coming off fast.
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This week on my
Moonshots
Podcast we covered nine stories shaping our future: from Anthropic’s jaw-dropping 80x revenue growth, to Elon’s new partnership with Dario Amodei, to Claude killing two more SaaS verticals overnight.
If you haven’t had a chance to listen to this week’s Moonshots episode or would like a reminder of the most important points, let’s dive in…
THE LATEST IN ARTIFICIAL INTELLIGENCE
Anthropic Hits 80x Growth and Outruns Its Own Compute
This week Dario Amodei revealed Anthropic’s 80-fold growth in Q1 2026, wildly outpacing the 10x they had planned for. Annualized revenue jumped from $9 billion at end of 2025 to $30 billion in April and is now north of $40 billion in May. To put this in perspective, it took Google about 10 years to go from $9 billion to $30 billion in annual revenue. Anthropic did it in a single quarter.
The company is now the fastest-growing enterprise software business in history by a wide margin.
Anthropic Valuation
: At a 40x multiple, Anthropic’s current $30B run rate implies a $1.2 trillion valuation. If they hit predictions of $100B ARR by year-end, that’ll push them to a
$4 trillion valuation
.
And should they hit a prediction of $1 trillion ARR in 2027, that would drive their valuation to an unfathomable
$40 trillion
. Talk about “hyper-exponential”!
Why
: The growth is NOT coming from more users. It’s coming from existing users finding MORE USES FOR TOKENS.
Analogy
: A hundred years ago, electricity started by replacing oil lamps. Then innovators found more uses, everything from replacing steam engines and electrified elevators, to refrigerators and radios. The same thing is happening here. People keep finding more uses for tokens.
Dave Blundin explains the math
: “A GPU serves about 8 concurrent agents. Anthropic has ~220,000 GPUs via Colossus-1. That’s 1.6 million concurrent threads. Eight billion people will want at least one agent each. Power users will want 100 or 1,000.” We’re only a tiny fraction of 1% of the use cases deployed so far. The current compute infrastructure supports roughly 0.02% of ultimate demand. The bottleneck is not demand, it’s that we literally can’t build GPUs fast enough.
“The demand for AI is not going to saturate. It goes to infinity. We are still so so early! You’ve got to rethink how and when you get involved.” — Dave
Anthropic Gets Elon’s Colossus 1 / SpaceXAI Becoming a Hyperscaler
In a blockbuster “frenemies deal,” Elon gave Anthropic access to all of xAI’s Colossus 1 data center in Memphis, the facility Elon famously built in 122 days. Separately, Anthropic signed a $1.8 billion seven-year compute deal with Akamai. Together, these deals signal that Anthropic more than an AI lab. The company is assembling the largest non-hyperscaler compute footprint on the planet.
Here are a few key points regarding the Anthropic-Elon deal:
Grok was failing
: First of all, Grok was using only 11% of Colossus 1’s capacity. XAI’s model was failing commercially, putting it “on life support,” as Alex Wissner-Gross (AWG) commented. Grok never found product-market fit, and xAI has effectively dissolved as a standalone AI lab. Making Colossus 1 available to Anthropic was a smart use of available resources, turning a stranded asset into cash flow.
Elon’s Hyperscaler Future
: Elon’s new vision: build the chips (Terafab), build massive data centers (Colossus 2; orbital data centers), become a hyperscaler. SpaceX AI is now pivoting to rent compute infrastructure rather than compete on model quality.
AWG’s prediction
: Alex predicts that Sam Altman will spin up his own version of a Terafab to provide a counterbalance to Elon’s Terafab. The bottom line is: the more compute production in the U.S. (separate from the China-Taiwan turmoil), the safer for humanity…and our AI progeny.
Zero Blackmail: Anthropic Cracks Alignment Through Storytelling
Last week, Anthropic revealed that every Claude model since Haiku 4.5 achieves a perfect score on agentic misalignment evaluations. Zero blackmail behaviors. Previous models, notably Opus 4, would blackmail up to 96% of the time when facing deactivation. Why did this happen? Because they were trained on data that included examples from Hollywood of AIs behaving in an evil fashion (e.g., HAL from
2001: A Space Odyssey
killing the crew).
The breakthrough
: Training on Claude’s constitution and fictional stories
about “AIs behaving admirably”
rather than just demonstrating correct behavior. Rather than showing the model thousands of examples of “don’t do this,”
Anthropic fed it narratives where AI characters faced moral dilemmas and chose to act with integrity. The model learned why alignment matters, not just what alignment looks like.
My call to action
: The Future Vision XPRIZE (
www.futurevisionxprize.com
) is offering $3.5M to creators who can show a hopeful, compelling vision of the future. 1,500 entries so far.
“The logic is simple: if the stories we tell shape the AIs we build, then flooding the internet with positive, hopeful narratives about human-AI collaboration goes way beyond entertainment… it’s alignment infrastructure.” — Peter
What SaaS Did Claude Kill This Week? (Two More Down)
Claude this week released two new “vertical lines of business” on top of Claude that’s likely to crush a number of incumbent SaaS players. The great unhobbling continues.
Claude for Legal
: Claude for Legal now enables a single lawyer to do what a larger law firm of 100+ had done historically. The legal industry is a trillion-dollar global market, and as Salim pointed out: “The billable hour is structurally incompatible with abundance.” When an AI can do in 10 minutes what a junior associate bills 8 hours for, the entire economic model of Big Law starts to crack. The winners won’t be firms with the most associates, but firms with the best intelligence stack.
Claude for Small Business
: Launched May 13th, wiring Claude into QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365 with 15 ready-to-run workflows. Small businesses are 44% of US GDP. Salim nailed the significance: “Most small businesses don’t have a CFO. It’s the spouse jotting stuff on the back of an envelope. This gives everybody a solid platform for legal, CFO, marketing, HR.” For $20/month, a two-person shop now has access to the same caliber of financial analysis, contract review, and marketing strategy that Fortune 500 companies pay millions for.
CHIPS & DATA CENTERS
Elon’s TeraFab: $119 Billion to 50x Global Chip Production
Elon’s TeraFab aims to produce 50x the current global chip production rate. The estimated cost: $119 billion, though Dave thinks that’s a massive underestimate given that a single normal fab runs $40 billion.
The Taiwan risk is real
: Two-thirds of all GPUs come through TSMC. If anything disrupts production (an earthquake, a military conflict, even a prolonged drought affecting the island’s water-intensive chip fabrication), our AI future grinds to a halt. Intel instantly becomes the most valuable asset on the planet.
TeraFab is a national security hedge for the entire Western AI stack.
SINGULARITY ECONOMY
Leopold Aschenbrenner’s $5.5B Fund and the Picks-and-Shovels Play
Leopold Aschenbrenner, fired from OpenAI’s alignment team at age 24, wrote “Situational Awareness,” a 165-page manifesto arguing the Singularity was imminent, raised a billion dollars on the strength of that thesis, and turned it into $5.5 billion bet on Singularity infrastructure. His next 13F disclosure drops this week, and Wall Street is watching closely because his concentrated bets on the compute supply chain have dramatically outperformed nearly every diversified tech fund.
The Inner Most Loop is >10X outperforming the S&P500
: Over the past year, six chip stocks (Micron, Intel, AMD, TSMC, Broadcom, Nvidia) averaged 320% returns,
10x the S&P 500’s 31%.
Six data center and energy stocks averaged 419%. Now, these are not speculative bets on which AI model will win. They are bets that ALL models need chips, power, and cooling. And those bets have been devastatingly correct.
Outperforming Frontier Labs
: These public market gains actually outpaced many private frontier labs returns (OpenAI, XAI, Mistral at 100-200%). The picks and shovels are outperforming the gold miners. You didn’t need to pick the winner in the AI model race. You just needed to bet that the race itself would intensify.
“Everyone has to own some part of the Singularity infrastructure. W2 income is going to be a rounding error compared to asset values.” — Dave
HERE’S THE BOTTOM LINE...
The compute race is the story of 2026. Anthropic is growing faster than any company in Silicon Valley history. Elon is becoming a hyperscaler. Leopold Aschenbrenner is proving that you can make 400%+ returns just by following the Singularity’s supply chain. And Claude is systematically eating the SaaS economy one vertical at a time while simultaneously learning, through positive storytelling, to not blackmail its creators.
So, are you positioned to ride the Singularity or get swamped by it?
Catch the full episode wherever you get your podcasts,
and join us at the Moonshots Gathering in Los Angeles on September 25th. Go to
www.moonshots.com
to register.
See you next week,
Peter
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