The Signal

Like many people drawn to the stock market, one of the first books I read was Reminiscences of a Stock Operator. It follows Jesse Livermore’s rise, crashes, and recovery, with a central lesson: success in markets requires patience, emotional discipline, and listening to the tape rather than fighting it. As someone who spends much of his life thinking several years into the future, that lesson has kept me honest. It forces me to think in bets rather than certainty and look for a signal.

Standard deviations or sigmas became a central part of my signal framework when I began as an options trader. I came to view them as a baseline for identifying moves large enough to suggest a potential change in trend, whether through capitulation or breakout. Combining that search for multi-sigma moves with Livermore’s respect for the tape and my preference of studying Elliott Wave patterns, I looked for moments when the larger pattern appeared to be approaching an inflection point and then waited for the market itself to tell me when conviction should become position size.

Bitcoin just delivered the kind of week that should make an investor stop and ask a different question. It rose roughly 22% against a one-sigma weekly move near 3%, about a seven-sigma move and broke above its 200-day moving average in the same week. That combination has appeared only twice in the last decade. Both prior episodes led to substantial further gains.

The pattern is evidence, never a mechanical price target. It signals that the tape is updating the story. The useful question is why this event is appearing now, after two years in which Bitcoin’s price has done little while almost everything around it changed.

Most people have focused on Bitcoin’s bear market since October. I have been more focused on those last two years. The first quarter of 2024 brought the launch of the spot Bitcoin ETFs, followed by the election of a U.S. president who embraced crypto. If Bitcoin ever had a classic “buy the rumor, sell the news” moment, 2024 was it.

Then came the inauguration and the meme-coin launch, making clear that many decentralization ideologues would be uncomfortable with the direction of travel. At the same time, AI offered the seduction of parabolic returns that had long drawn capital into crypto. The result was a two-year consolidation: early believers and ideologues had reasons to sell, while ETFs, new products, and political acceptance steadily expanded the potential buyer base.

This two-year period is what I have called Bitcoin’s silent IPO. Bitcoin never filed an S-1. It had no management team, underwriters, roadshow, earnings estimates, or conventional valuation model. Yet it has undergone the economic equivalent of an IPO: a long transfer of ownership from early believers and concentrated holders in the private market toward a broader, more institutional, regulated buyer base.

The last two years were not empty time. They were time for distribution, absorption, and acceptance. ETFs made ownership easier. Custody became more institutional. Political acceptance broadened. Products and infrastructure gave conventional pools of capital a way to own Bitcoin without first becoming crypto natives. At the same time, some of the people who got there earliest did what rational owners of a wildly appreciated asset do: they rebalanced, monetized, or turned toward the next frontier, especially AI. That selling did not necessarily express lost conviction. Often, it reflected a change in portfolio arithmetic. When a small allocation becomes a dominant share of wealth, reducing it is risk management.

This is what an IPO does. It distributes an innovation. It changes who owns the asset, who can own it, and what kind of capital sets the marginal price. The process can be volatile and emotionally unsatisfying because the owners who saw the earliest promise are not always the owners who finance the next phase. But the change in ownership can be the condition that makes the next phase possible.

Bitcoin’s current move therefore matters in context. It arrives after the distribution. It arrives after institutional pathways have been built. And it arrives as the world is discovering that Bitcoin was never only the story. Bitcoin was the gateway to a much larger financial architecture, one that may become increasingly necessary as AI changes the speed of economic life.

The Crypto Gateway

Bitcoin’s history reaches beyond Bitcoin itself. It established that scarce digital value could be created, owned, and transferred over the internet without requiring a central intermediary to validate every exchange.

The Bitcoin white paper solved the double-spending problem in a practical way. In the physical world, a dollar bill cannot be handed to two people at once. In the digital world, copying information is trivial; preventing the same digital unit from being spent twice had required a trusted intermediary. Bitcoin offered another method: a distributed network that could agree on ownership and transaction history without relying on a bank, broker, or state database as the sole recordkeeper.

That breakthrough opened a gateway. It made digital bearer assets imaginable. It made the transfer of unique digital property possible. It created a new way to think about trust, ownership, settlement, and collateral on the internet.

Marc Andreessen saw this clearly in his 2014 essay, Why Bitcoin Matters. He described the applications that could follow from a new form of digital property: digital contracts, keys, ownership of physical assets, stocks and bonds represented digitally, global payments, micropayments, and money that could move with far less friction. Bitcoin introduced a new financial and computational primitive.

It took time for that primitive to become an ecosystem. New technologies do not generally eliminate the old system and then begin immediately from a clean slate. They enter the old system, expose its limitations, and slowly merge with it. Personal computers did not instantly replace mainframes. The internet did not instantly replace retail, media, or finance. Cloud computing did not instantly replace enterprise servers or software. The old and the new coexist until the new architecture becomes useful enough, trusted enough, and simple enough to alter the behavior of everyone around it.

Bitcoin has increasingly become the monetary foundation of this ecosystem: a scarce, global, digitally native store of value that can function as collateral across time. The broader crypto ecosystem has pursued the applications implicit in the original breakthrough. Stablecoins make dollars programmable and continuously transferable. Tokenization makes ownership and collateral machine-readable. Smart contracts make rules executable. Wallets create native accounts for people and eventually for software agents. On-chain systems make settlement, auditability, and permissions capable of becoming software rather than a sequence of emails and reconciliations.

Not every application will work. Not every token will matter. The point is larger: Bitcoin opened the door to a financial architecture built for the internet. The next question is what happens when the internet is no longer populated primarily by people clicking buttons, but by agents that can research, negotiate, execute, pay, and reallocate continuously.

Human Time Was the Old Operating System

Artificial intelligence is the first technology that manufactures more time on one side of the ledger while extending it on the other. It manufactures time because digital agents can perform an expanding share of economically useful work without sleeping, commuting, losing focus, or waiting for Monday. It extends human time because the same accelerating tools are being directed toward biology, diagnostics, drug discovery, and diseases of aging as we saw with Moderna’s cancer vaccine this month.

We are still arguing in the wrong units. Whether models truly reason, whether every benchmark is meaningful, and whether artificial general intelligence arrives in one year or ten are consequential questions. But the operative fact is already visible: capability is advancing faster than the institutions built to absorb it.

AI disrupts through time. It changes the speed at which work, competition, and adaptation occur.

Every structure through which we organize economic life is a synchronization protocol for human bodies: the quarter, the fiscal year, the annual budget, the four-year political cycle, the four-year degree, the thirty-year mortgage, the forty-year career, and the discounted cash-flow model with a terminal value attached to its end. None was derived from an eternal principle. Each was calibrated, explicitly or implicitly, to how long it takes a person to learn, concentrate, coordinate, decide, execute, recover, and age.

For most of history, human time was the hidden governor of output. A firm moved only as fast as its people could research, communicate, write code, reach consensus, and deliver. Even exceptional organizations were constrained by meetings, distance, fatigue, hiring, managerial span, and the basic difficulty of getting people to act together. Quarterly reporting existed because closing the books took time. Annual budgets existed because allocating resources demanded deliberation. Markets closed because the people running them needed rest.

Human time was the operating system of industrial capitalism.

The useful measure of AI is the amount of skilled human work time a system can reliably compress. We are already moving toward that language: asking how long a task would take a capable human and whether a model can complete it with sufficient reliability. Human time has become the denominator of the machine world.

The slope will be debated, and it should be. Reliability matters more than isolated demonstrations. But the direction is clear. Systems are moving from answering questions, to completing bounded tasks, to running longer chains of research, code, testing, analysis, and execution. The question is not whether every job disappears tomorrow. It is what happens when the share of valuable work that can be delegated compounds year after year.

The emerging production function is therefore changing. It is no longer only labor and capital. It is capital, energy, compute, tokens, and human judgment. Tokens are becoming units of machine cognition and machine labor. They are not merely a technical metric. They represent an increasing share of work that used to be supplied through human hours.

Organization Versus Fleet

A well-run knowledge worker may deliver roughly 2,000 paid hours a year, and materially fewer once meetings, context switching, coordination, and recovery are removed. A digital agent has 8,760 hours of calendar availability. Parallelism supplies the larger multiplier.

You do not hire an agent in the historical sense. You release ten, one hundred, or one thousand. If you still can’t imagine this, sign up for Grok Bot for one month and see. They can test competing hypotheses, write alternative implementations, monitor operations, reconcile accounts, review documents, search for anomalies, and begin again continuously. The binding constraint begins to shift away from recruiting, office space, and managerial span. It becomes compute, data, energy, capital, and the quality of the person or system directing the fleet.

The relevant comparison is no longer person versus model. It is organization versus fleet.

A company operating a capable fleet against a company operating through conventional staffing is not simply twenty percent more efficient. The two are on different clocks. One remains bound by biological coordination. The other has begun to compound machine execution. The divergence is geometric rather than linear.

The human premium migrates upward: to judgment, problem formulation, taste, trust, accountability, and capital allocation. Machines compress the distance between a decision and its execution. People determine the objectives, limits, responsibilities, and values behind the work.

But that distinction should not minimize the economic change. A competitor can now build, test, distribute, and improve a product inside a period that formerly would have been spent scheduling the kickoff meeting. Research itself develops a latency problem. An analyst may spend six weeks understanding an industry only to find that a new model release, pricing change, or agentic workflow has altered the competitive landscape before the work is published. Diligence becomes stale inside its own window.

That is why AI is ultimately a time problem for investors. It can improve earnings, raise productivity, and create entirely new markets. It can also reduce the trusted duration of an advantage. This is why Charlie Munger famously said “technology is a killer as well as an opportunity.” A company may be excellent today and still be difficult to underwrite three years from now if the half-life of its moat is declining faster than the discount rate can compensate.

Compression Meets Atoms

The compression arrives in sequence, and the sequence matters because each step moves closer to the physical world. This is why my first focus from an investment perspective for AI has been the infrastructure to build tokens and feed the agents.

Agentic coding comes first because software is the most malleable part of the economy. An agent can read documentation, write code, run tests, find failures, fix them, deploy, and begin again without waiting for the next business day. That is not merely a software-industry story. Software is embedded in finance, logistics, manufacturing, medicine, media, retail, defense, and energy. If the time to build and improve software collapses, so does the time required to redesign every industry software touches.

Consumer and enterprise agents follow, compressing transaction time: research synthesis, procurement, customer service, contract review, compliance monitoring, reporting, and administration. Then AI moves toward the physical world through world models, autonomous vehicles, industrial automation, robotics, and humanoids. A fleet can collect data overnight, simulate edge cases in parallel, and distribute improved capability to every deployed unit. One machine’s lesson can become every machine’s lesson.

Atoms keep their own clock. Compute cannot repeal it.

Concrete cures on its schedule. Turbines, transformers, transmission lines, fabs, cooling equipment, and data centers involve multi-year lead times. Permits, interconnection queues, construction, and public consent often take longer. Biology still requires validation. Trust, due process, and legitimacy cannot be generated by inference.

This unevenness is the thesis. As cognition becomes more abundant, the bottleneck migrates: from cognition to compute, from compute to energy, from energy to physical throughput, and finally to institutions. The last layer may be the slowest because it is made of consent and slowed by the bureaucracy of enterprises.

The gap between digital capability compounding in months and physical capacity moving in years is where much of the next cycle’s dislocation will live. It is also where the opportunity lives: energy, grid infrastructure, advanced compute, networking, data centers, specialized materials, and scarce collateral matter because they do not compress at the speed of software.

GDP Was Built for Human Time

Productivity is a time equation. GDP was designed for an economy dominated by physical output and human labor delivered in defined periods. It records factories, construction, wages, and market transactions extremely well. It sees less of the value created when intelligence is delivered at near-zero marginal cost, software improves continuously, and an agent compresses a week of professional work into an hour.

The result is a measurement gap. The national accounts sample a compounding digital economy through quarterly and annual schedules. Finance settles continuous commerce in batch windows. Markets process continuous change around quarterly disclosure. GDP remains useful; its view is increasingly late and incomplete. Policymakers can read muted productivity, soft employment, or backward-looking inflation while digital output accelerates underneath the surface.

Financial Time Has to Catch Up

AI can compress the time required to create value. Economic time will not follow unless the financial system can move value at a comparable speed.

Finance still runs through batch processes, settlement windows, banking hours, fragmented payment rails, manual compliance, and backward-looking risk review. Those structures were rational when information and commerce moved at human speed. They become a hard constraint when agents can negotiate contracts, manage inventory, extend credit within limits, rebalance portfolios, and pay suppliers continuously.

The rails of an AI economy need to be continuous: real-time settlement, programmable payments, tokenized collateral, automated but constrained credit, always-on markets, and machine-readable ownership and compliance. This is where the crypto ecosystem becomes practical rather than ideological.

Stablecoins can provide programmable dollar settlement across borders and time zones. Tokenization can represent claims, collateral, and ownership in forms that software can read and act upon. Smart contracts can execute conditional rules. Wallets can become native accounts for people and eventually agents. On-chain systems can make audit trails and permissions available in real time rather than after a chain of reconciliations.

The objective is to build guardrails into high-velocity rails. Identity, custody, permissions, collateral, auditability, compliance, and risk limits must operate at machine speed if agents are to act economically inside a trusted system.

Bitcoin’s place in this architecture is distinct. Bitcoin may not settle every agent transaction. Stablecoins and specialized rails may do much of that work. But Bitcoin remains the proof and the foundation: a scarce, globally transferable digital bearer asset outside the discretionary expansion of any one credit system. It can become increasingly relevant as neutral collateral and long-duration savings while more transactional layers of crypto evolve above it.

The architecture has layers. Bitcoin Layer 1 can serve as the scarce collateral and final-settlement anchor. Stablecoins, tokenized deposits, Layer 2 networks, and other programmable rails can handle the high-frequency velocity of a machine economy. Every agentic micro-transaction does not need to occur on Bitcoin’s base layer. The system needs a trusted monetary foundation and faster rails for continuous activity.

AI creates the need for machine-speed economic agency. Crypto provides the emerging rails. Bitcoin provides the monetary foundation and the digital store of value in a world of hypercompetition and disruption.

The Debt Market Meets the AI Capital Cycle

This collision is occurring inside a credit-backed fiat system with its own time problem. Debt finances present spending and investment through claims on future income, taxes, and output. That structure rests on confidence that tomorrow’s economy will be legible enough to underwrite promises made today.

Governments face a duration trap. They refinance long-dated fiscal obligations while AI shortens the life of the assumptions beneath them: the tax base, labor market, corporate profit pool, and durability of competitive advantage. At the same time, the physical AI buildout requires capital now, chips, compute, data centers, cooling, generation, transmission, land, and construction. Long rates reflect fiscal supply, inflation expectations, monetary policy, growth, global savings, and term premia. AI capital spending adds to the pressure by increasing the competition for capital, energy, and physical capacity. Debt is a claim on future output. AI is making the structure of that future harder to model.

Scarce, globally transferable collateral rises in value when long-dated financial assumptions become less comfortable. Bitcoin becomes a claim that the next monetary regime will be harder to manage precisely because the future is accelerating.

Terminal Value, Liquidity, and the New Market Clock

AI accelerates creation, but it also accelerates destruction. Schumpeter’s cycle was historically paced by slow things: capital cycles, labor retraining, asset depreciation, physical distribution, and the gradual diffusion of technology. Many of those brakes are weakening.

A better workflow can be copied at close to zero marginal cost. A new entrant can build against an incumbent’s weakness before the incumbent’s next earnings call. A company may not have years to respond to a technology shift. It may have only a few capability cycles.

Terminal value becomes the weakest number in the model. A discounted cash-flow analysis assumes that competitive advantages decay slowly enough for a perpetuity to mean something. When the half-life of a moat falls faster than the discount rate compensates, terminal value becomes a spreadsheet representation of a calendar that no longer exists.

Markets add a second problem. Information arrives continuously but is priced discontinuously. Product cycles may run in weeks, but disclosure still runs in quarters. The market accumulates unpriced change, then reprices it all at once around an earnings print, product release, benchmark result, or evidence that a bottleneck has moved.

Liquidity is a promise that there will be enough time to transact: to find a buyer, reduce risk, meet a margin call, unwind a position, or change one’s mind without materially moving price. Leverage is the same promise in another form. It assumes the investor will have enough time to respond before volatility, financing costs, or redemptions force the decision.

AI weakens both assumptions. New information can propagate through agents, algorithms, systematic strategies, social networks, and options markets faster than a traditional investment committee can convene. A thesis that once unwound over quarters can be challenged in a session. The market does not need to conclude that a company’s earnings vanish. It only needs to decide that the period of excess returns is shorter than it previously believed.

The sequence is straightforward. A question about a moat, the return on compute, or the capital intensity of a theme leads to multiple compression. Positioning adjusts. Leveraged capital cuts exposure. Systematic strategies respond to volatility. Hedges are bought into a rising volatility surface. Dealers adjust. Correlations converge. A valuation debate becomes a liquidity event before the fundamental debate is fully settled.

July demonstrated the mechanism. Demand for compute, power, and infrastructure did not have to soften for a sharp momentum unwind to occur. A concentrated and crowded positioning structure was enough to compress what might historically have been a multi-quarter correction into days of forced repositioning. The fundamentals were intact; the clock available to capital was not.

The next genuine challenge to the AI thesis, whether about capital spending, platform economics, physical bottlenecks, or terminal value, will arrive into a market with less time to process it than historical frameworks assume. July supplied the mechanism. A credible motive makes the adjustment faster and more severe because margin desks may settle the timing before analysts settle the argument.

The speed of AI turns a valuation question into a market-structure event.

This is where I lean into the controversial playbook of Michael Saylor who understands this trap better than anyone. He saw the connection of the time disruption due to exponential innovation and the hedge Bitcoin provided before anyone else in public equities. MicroStrategy had succeeded as a software company and accumulated significant cash, yet Saylor concluded that a midsized firm could not outcompete Microsoft, Google, or Apple for talent, capital, and product velocity. At the same time, the Federal Reserve had pushed rates toward zero during Covid, leaving that cash exposed to the debasement he believed would follow without offering a credible way to reinvest it inside the existing business. His decision to put Bitcoin on the balance sheet looked radical and remains controversial.

The underlying logic was straightforward: when a business faces a faster and better-capitalized competitor, retained capital can become trapped between disruption on one side and monetary dilution on the other. Saylor chose scarce digital property as the hedge. That is why his decision matters beyond Strategy. AI is about to confront every company and every investor and allocator around the world with some version of the same question: does the capital in this portfolio have a plausible path to outpace exponential competition, or does it depend on a duration of cash flows, infrastructure, and market power that the new clock will not grant? Saylor’s line, “You don’t find Bitcoin, Bitcoin finds you”, is an insight anyone who has lived through emerging-market currency regimes or hyperinflation understands immediately. It resonates with me because of my time in Brazil up until the devaluation. Bitcoin becomes relevant when the existing map of opportunity no longer offers a durable place to store the value already created.

The Economy Is Changing Clocks

The evidence has moved beyond benchmarks, demos, and venture-capital narratives. It is in the speed. The capital required for data centers, power systems, networking, chips, and token factories is rising at a pace the old economy was never designed to finance or physically deliver. Revenues at Anthropic and OpenAI are scaling at a pace the old economy was never designed to measure.

The larger story extends through the ecosystem. AI-native businesses are being formed, launched, distributed, and scaled faster than earlier generations of companies could hire their first meaningful team. A product can be built, tested, marketed, sold, supported, and improved by a small number of people directing a growing fleet of agents.

Five years from now, the relationship among labor, capital, consumption, production, and financial intermediation may be recognizably different because the time required to organize and execute valuable work has collapsed. Agents will increasingly influence consumption, research, procurement, portfolio decisions, pricing, and capital allocation. Crypto is becoming the guardrail architecture for finance operating on a clock built for machines.

The transition will feel violent. Company-level recession can coexist with economic acceleration: more output, more capability, more new businesses, and more earnings power alongside rapid destruction of business models, skills, and claims on the future. Companies that survived through patient capital, slow competition, and long adjustment periods will face a new question: can they compete three years from now? That uncertainty drives terminal-value scrutiny, multiple compression, company-level volatility, and pressure around long-term rates. A new economic species is emerging, one that creates, decides, consumes, and transacts at a speed the old system cannot easily absorb.

This is where investors and savers need to think in bets. Agreement with every part of the Bitcoin thesis is unnecessary; preparation for a change in time is essential. Portfolio construction has to change with the clock. Most portfolios remain built for the old economy: ownership claims on large corporations whose value rests on durable moats, layers of intermediation, and long horizons for excess returns. They own friction toll takers on economic activity organized around human consumers, human workdays, human settlement cycles, and human bureaucracy. AI places all of those assumptions under pressure.

The crypto economy, the financial guardrails of this new AI time-altering world, still carries a smaller aggregate value than many individual incumbents it could disintermediate. Tokenization builds the bridge between the systems. Ownership can be represented digitally. Collateral can move continuously. Businesses can distribute payments, fees, and economic rights automatically through smart contracts rather than through successive intermediaries. Public equity remains valuable, yet it remains a claim mediated through corporate structures, banks, exchanges, clearing systems, administrators, and legal processes built for a slower world. The disruption of those middlemen is arriving faster than most allocation frameworks acknowledge.

This is why Bitcoin matters as a hedge against a world of abundance. AI makes intelligence, software, analysis, and competition more abundant. That abundance can be extraordinary for society while creating deep disruption for holders of assets whose value depends on scarce capability, stable market share, or terminal value projected far into the future.

Charlie Munger warned that technology is “a killer as well as an opportunity.” The irony is that one of the great advocates of moat investing also called Bitcoin “rat poison” in 2013 and, after its next parabolic rise, “more expensive rat poison” in 2018. Warren Buffet later added that Bitcoin was rat poison squared when it was below 10,000. AI is making Munger’s and Buffet’s insight central to portfolio construction. The critical question shifts from who can grow fastest to what can survive the speed of competition in a moatless world.

Bitcoin offers a different kind of claim. It is a globally held belief in fixed scarcity, reinforced by a distributed network that sits outside the corporate and sovereign claims competing for the future. It is not a U.S. political trade, a management team, or a business model requiring protection from the next technological leap. Its value rests on the conviction that twenty-one million units remain scarce while the world around them becomes more abundant, more programmable, and less predictable. When the useful life of companies, moats, and financial assumptions is shrinking, a store of value becomes more important. Bitcoin is the asset designed to preserve purchasing power through time when the future itself is becoming harder to underwrite.

Bitcoin and Time Risk

Bitcoin’s technical breakout signals a wider macro transition. Bitcoin has spent two years moving from early concentration toward broader ownership, from ideological framing toward institutional acceptance, and from a standalone asset story toward the monetary foundation of a wider crypto ecosystem. The silent IPO marks the beginning of a new phase. Bitcoin was accepted by the old system as that system entered a forced adaptation to a new clock. I wrote about this collision in The Yen Signal: a warning that the old system of sovereign debt, managed currencies, and gradual policy adjustment was beginning to meet a new system of machine-speed capital allocation, AI-driven investment, and programmable value.

Bitcoin hedges time risk: the risk that terminal values, fiscal assumptions, and conventional claims on the future become less durable than investors expect. Its proposition is fixed scarcity, global transferability, and final settlement across time. In an economy where the future is harder to underwrite, preserving value through time rises in importance.

The white paper gave the world a technology for scarce digital money. The broader crypto ecosystem is carrying the transactional and programmable innovation forward. Bitcoin has evolved into something more elemental: a globally held belief that scarce, neutral value can outlast the institutions, companies, and financial assumptions being disrupted around it.

Time was always the ultimate scarce asset. AI manufactures more of it on one side of the ledger while potentially extends it on the other. Crypto makes value capable of moving on that faster clock. Bitcoin offers digital scarcity that does not require the future to resemble the past.

Anyone measuring synthetic velocity in human calendar time is trying to clock light with an hourglass.

As a contrarian who believes Bitcoin is entering a third wave in an Elliott Wave pattern, I also appreciate the sentiment symmetry of its $58,000 low by someone who is the definition of the old system. Jeremy Grantham, one of the great value investors of the old system, described Bitcoin on CNBC as worth “less than a bucket full of piss.” If that low holds as part of the beginning of a third wave, the remark may become a useful historical marker: the moment a value investor measured a network built for a new monetary regime with the accounting standards of the old one and found the bucket easier to value.

I joined Morgan Stanley in 1992, just as arguably the greatest asset bubble of the twentieth century was deflating. Japan’s stock market had peaked, land prices were rolling over, and the country was beginning a journey that would define global macro for the next three decades: the slow accumulation of government debt to levels economists have repeatedly argued would be unsustainable. At the time, Japan was the outlier. Today, the government debt virus has spread across most of the developed world.

In the years following the bursting of Japan’s asset bubble, macro investors would repeatedly return to the same trade: short Japanese government bonds on the assumption that the country’s fiscal trajectory would eventually force a repricing. The trade became known as the widowmaker because the expected reckoning repeatedly failed to arrive. Japan ultimately taught an entire generation of traders how long a government that is effectively bankrupt and weak can survive without the debt condition improving.

That history matters because of what happened the last week of July. I was living in Brazil in 1998, and last week brought back memories of that year. The circumstances today are clearly different, but there were enough connections to believe it was an important macro inflection point, just like in 1998.

In June 1998, with the Asian financial crisis still spreading around the world and impacting every emerging market with a debt problem, we had a similar situation. At the same time, like today, the yen was weakening and applying pressure to a then weakening macro backdrop. The United States and Japan intervened jointly in the foreign-exchange market to support the yen. Within months, Russia defaulted, Long-Term Capital Management collapsed, and the unwind of leveraged yen-funded carry positions contributed to one of the most violent currency moves of the decade.

Fast forward to the last week of July this year: a hedge fund blow up, a new Fed Chairman losing some credibility on how hard he truthfully wanted to fight inflation with a bloated balance sheet, and then, closing out the week, the first coordinated yen intervention by the US and Japan since 1998.

Those events, and the reminder for me of 1998, are why I view last week’s intervention as something we will look back on as a contextual signal of the nexus point the world is in right now and where it is headed. I say contextual because the events occurred with global equity markets at or near record highs and earnings and profit margins growing rapidly. Credit spreads are near all-time tights. The VIX is calm. There is no obvious recession, banking panic or broad credit event forcing policymakers into emergency action. Yet the United States still concluded that the deterioration in the yen was important enough to join Japan in supporting the currency for the first time since my time in Brazil. In a market environment where most traditional measures of risk continue to appear benign, that decision stands out. The move itself is less important to me than the context in which it was made. There is no precedent for the world we are living in. This week represents exactly why my service is focused on the nexus between the aging, credit-based fiat system, AI, and crypto. For me, the last week of July was a nexus point, a moment when the collision pressures between these three forces became increasingly visible.

This gets back to the widowmaker reference. What stands out is that the timing of this nexus point coincided with the largest monthly rise in US 30-year yields since the new administration took over. It also happened to be the highest monthly yield close in over 20 years. We have learned over the last two years that their line in the sand appears to be a rise in long-term yields.

Yields have become one of the defining constraints on U.S. macro policy. When long-term yields rise materially, the move does more than tighten financial conditions. It increases the cost of servicing an already large stock of federal debt and raises the hurdle rate for private-sector investment, in particular the capital needs for the geo-politically important AI infrastructure buildout. It also pressures housing and other duration-sensitive sectors, exacerbating the K-shaped economy, and increases the amount of interest expense that must ultimately be financed through still more government borrowing. The higher yields go, the more fiscal policy and monetary policy begin to interact with each other.

We have repeatedly seen that large upward moves in long-term yields eventually generate a policy response of some kind. The response does not necessarily come through a traditional Fed rate cut. It can occur through liquidity measures and new liquidity facilities, changes in Treasury issuance, regulatory adjustments, central-bank communication and now, as we saw last week, coordination in the foreign-exchange market. The specific mechanism matters less to me than the recurring pattern: the administration has become increasingly sensitive to sustained increases in the cost of capital, and it appears to be its line in the sand. As I have said regarding the AI capex boom, we are running hot into compute scarcity. For the Fed and Treasury, we are running hot into a scarcity of tool options to fight long-term yields.

This is why the US-Japan coordinated intervention becomes particularly important. Japan is one of the world’s largest pools of savings and a major holder of U.S. financial assets. Japanese investors constantly make relative-value decisions between domestic bonds and foreign assets based on yields, currency levels and hedging costs. A rapidly weakening yen alongside rising Japanese yields and rising U.S. yields can alter those calculations significantly. At a moment when the United States needs enormous amounts of capital to finance its fiscal deficits, instability in the currency of one of its most important creditor nations is not an isolated Japanese issue.

That is why the U.S. participation in the intervention deserves far more attention than it has received. The important question is not simply why Japan wants help with a weakening yen. That is obvious. The more interesting question is why the United States decided that Japan’s currency problem had become an American problem. When both the borrower and the lender are burdened by debt, the relationship stops functioning like a normal credit system. That is the world we have arrived at now since the last time they worked together in 1998.

The rest of the market action in late July makes that question even more relevant. The Federal Reserve left markets unusually uncertain about the path of policy. Kevin Warsh has moved away from the traditional reliance on forward guidance and has indicated a greater willingness to allow markets themselves to determine financial conditions. At the same time, long-term yields were moving sharply higher. In theory, allowing the bond market to perform some of the Fed’s tightening work makes sense. In practice, the ability to tolerate significantly higher long-term yields becomes more complicated when the federal government’s interest expense is already rising rapidly. That is why the Treasury decision to intervene just two days after the Warsh comments is important, especially in the context of him recently being chosen by the administration amidst questions around Fed independence.

Then there was the extraordinary reversal in momentum and AI-related equities in July. This may not have been at the scale of LTCM, but the factor volatility rise and momentum fall was historic. The important point, in my view, is that the selloff was not driven by a corresponding deterioration in the fundamental AI story or in the broader economy. Demand for compute remains exceptionally strong and, in my words, insatiable. Hyperscaler capital spending remains elevated, backlogs remain enormous, and the underlying technological progress continues. What changed was crowded positioning, leverage and vol-controlled strategies at record gross hedge fund leverage. In a financialized world where government debt is a virus around the world and US stock market cap to GDP is over 200%, stocks, like long-term bonds, are not allowed to fall for long.

Crowded exposure, leverage and factor concentration turned a fundamentally healthy theme into the center of a violent market adjustment. That distinction matters. Market structure is going through a change: markets increasingly contain enormous pools of capital using similar data, similar risk models and increasingly similar AI-assisted analytical tools. When positioning becomes crowded, a relatively modest change in price can trigger automatic vol-controlled risk reduction across many portfolios simultaneously. The speed of the resulting move can become disconnected from the speed at which the underlying economic fundamentals are changing. LTCM took a long time to play out. The Situational Awareness fall took weeks, from a fund up hundreds of percent.

This is one of the larger changes taking place in global markets. AI is increasing the speed at which information is processed and incorporated into prices at precisely the same moment that government debt is reducing policymakers’ tolerance for large moves in interest rates and financial conditions. Those forces are becoming increasingly interconnected. Technology is accelerating market behavior while fiscal constraints are making the financial system more sensitive to the consequences of that acceleration.

The result is a market structure in which deleveraging becomes increasingly difficult for policymakers to tolerate. Governments need nominal growth to manage large debt burdens, but inflation remains high enough to constrain traditional monetary easing. Because of the debt burden, central banks have less freedom to fight sticky inflation with aggressive rate hikes, while governments have less ability to tolerate the economic damage created by substantially higher long-term yields. That leaves policymakers increasingly dependent on alternative mechanisms for managing financial conditions.

On Friday, we received another weak payroll report. At the same time, AI continues to surprise almost everyone with the speed of its exponential growth. Anthropic’s model capabilities and adoption, for example, have driven ARR growth at a pace the world has rarely, if ever, seen. Despite what your favorite economist may tell you while looking through a historical lens and assuming the old relationships still hold, something has changed. AI is already disrupting the labor market, and the rise of AI agents is only beginning.

Again, looking at the labor market contextually, it is very weak. Historically, with S&P 500 earnings growing this fast, job creation is normally robust. Right now, the six-month rate of change in aggregate payroll, combining hourly earnings, hours worked, and the number of jobs, is at its weakest non-COVID level since 2012, while earnings are growing at a post-stimulus pace. The labor force participation rate has fallen sharply this year and wages are falling. This all started at the unofficial beginning of AI agents, digital employees, with the rise of OpenClaw followed by Hermes. Economists academically try to show numbers on how AI is not causing job losses, but aggregate hours, wages and surveys show this is more about a lack of hiring while nominal GDP, revenues and earnings grow sharply.

Look at the labor market through that AI disruption lens, combine it with the late July events, and it looks less like three unrelated stories and more like different expressions of the same underlying tension. A crowded AI trade experienced a violent positioning unwind despite strong fundamentals. The Federal Reserve left investors uncertain about how it intends to balance persistent inflation against rising long-term borrowing costs. Treasury yields moved sharply higher as fiscal concerns remained unresolved. And in the middle of it all, the United States joined Japan in a coordinated effort to stabilize the yen.

Don’t read this paper as a suggestion that another 1998-style deleveraging event is imminent. In 1998, the fault line ran through emerging markets, where weakening currencies and unsustainable debt burdens ultimately required IMF intervention. The situation today is fundamentally different. This time, the United States is helping Japan manage the consequences of its debt burden at least partly because instability in Japan can feed directly back into U.S. debt markets. In other words, the intervention is not simply about helping Japan with its problem. It is also about protecting the global capital flows the United States increasingly depends on to finance its own.

That may be the most important signal from last week. Stock markets remain near record highs, yet policymakers behaved as though something in the global financial architecture required attention. The intervention suggests that the interaction among currencies, sovereign yields, and cross-border capital flows has become important enough to warrant coordinated government action even in the absence of an obvious financial crisis.

For me, this is not being driven by a hidden leverage crisis like 1998. It is being driven by the nexus between an aging, credit-backed fiat system already under pressure and the accelerating disruption of AI, including the enormous capital requirements needed to support it. The problem is that these two forces are moving at very different speeds. AI is advancing exponentially, while the financial and policy architecture being asked to fund and absorb that change was built for a much slower world.

I always look to asset prices for confirmation that we may have reached an important nexus point, and this week that confirmation showed up in gold. Gold rallied more than 7%, one of its strongest weeks since the GFC, immediately following the events of the final week of July. Almost as quickly, Bessent publicly suggested that the Federal Reserve consider expanding the FIMA repo facility, which would allow Japan to raise dollars against its Treasury holdings rather than sell those securities into the market. These are not conventional policy responses for an environment in which equities are near record highs and credit spreads remain historically tight. FIMA is not literally money printing, but economically it belongs to the growing set of balance-sheet mechanisms designed to prevent forced asset sales and preserve liquidity when stresses emerge. At a minimum, it is a verbal bazooka indicating they are scared.

That is why I view gold’s move as more than a reaction to weaker payrolls or shifting Fed expectations. Macro participants are recognizing that the debt overhang is increasingly forcing policymakers toward some version of the same answer: keep the system running hot while developing additional hidden liquidity tools to manage the consequences. Gold is the asset class most naturally positioned to ask whether maintaining the stability of the sovereign debt system will ultimately require more liquidity, more financial repression, and a continued tolerance for nominal growth and inflation running hotter than the old framework would have allowed.

As Lyn Alden has argued in a different context, nothing stops this train. Governments are trapped by the size of their debt burdens and, in the US case, its deficit. They need nominal growth, productivity and asset appreciation to outrun the mathematics of the debt.

The institutional details reinforce that conclusion. Treasury’s willingness to discuss raising the relevant FIMA cap in order to facilitate Federal Reserve participation in the dollar-yen operation suggests a degree of coordination between Treasury and the Fed that may be greater than investors appreciate. It does not mean the two institutions have identical objectives, but it does highlight how difficult it has become to separate monetary policy, fiscal policy and financial-stability policy when sovereign debt levels are this large.

That has an important implication for the Fed. If Treasury and the Federal Reserve are increasingly operating within the same constraint set, large deficits, rising interest expense, inflation that remains too high for unrestricted easing and a financial system that cannot easily absorb uncontrolled deleveraging, the range of genuinely hawkish policy outcomes becomes narrower. A Fed that allows long-term rates to perform the tightening may discover that the fiscal consequences of those higher rates eventually force policymakers back toward intervention.

The next phase of that collision is likely to bring renewed fears of currency debasement. Governments are carrying debt burdens and fiscal deficits that become harder to manage as long-term interest rates rise. Interest rates are rising because governments are running hot into scarcity in the global AI race while the capital needs to fund it grow. It is unlikely the actions between Japan and the US will stop the pressure. Dollar Yen has become a new pressure point for the market to watch. At the same time, they cannot easily tolerate the kind of deleveraging that would normally accompany tighter financial conditions. The path of least resistance therefore continues to point toward liquidity: new facilities, balance-sheet mechanisms, financial repression, and ultimately policies designed to keep nominal growth running faster than the debt burden. Gold’s move this week may be the market’s first acknowledgment that the solution to the debt problem will increasingly look like some form of debasement.

AI will only intensify the tension. The disruption in the labor market is still in its earliest stages, and the rise of AI agents is only beginning. The first half of the year investors focused on the infrastructure needs to support those agents. In the second half, I believe the adoption and actions of the agents become the important investment thesis.

Over the next twelve months, I expect agents to move rapidly from tools that assist humans to systems that increasingly act on their behalf. That means more productivity, but it also means more pressure on employment, wages, tax receipts, and the political response required to manage the transition. The economic system will simultaneously be asked to finance unprecedented investment in compute infrastructure while adapting to a technology capable of reducing the need for human labor across an expanding number of industries.

The next step is where this becomes even more interesting. Consumer agents are about to enter the financial system. They will search, negotiate, purchase, move money, allocate capital, and transact at a speed and frequency humans never could. That should increase the velocity and volume of economic activity, but it also creates a new problem: the financial guardrails of the analog economy were designed around human beings making decisions, not billions of autonomous software agents conducting transactions continuously.

That brings me to the next intersection in the AI Macro Nexus: crypto.

If AI is creating a digital economy increasingly populated by autonomous agents, then that economy will require digitally native money, collateral, settlement, identity, and financial infrastructure. At the same time, if the response to the debt burden of the existing fiat system increasingly requires liquidity creation and currency debasement, then scarce digital assets become more relevant, not less. These two forces are approaching each other from opposite directions.

That is why I am spending more time now on this next phase in my videos and writing. The first phase of the AI Macro Nexus was understanding the physical infrastructure required to create intelligence. The next is understanding what happens when that intelligence begins acting autonomously inside an aging financial system that was never designed for it. This becomes important for crypto and Bitcoin.

The yen intervention was not the crisis. Gold is not yet signaling a crisis. AI agents have not yet transformed the economy. But the pressure from all three is beginning to show up at the same time. That is the nexus point. And I believe we will look back on the last week of July as one of the moments when the merging of the credit-backed fiat world and the AI-fueled digital economy stopped being theoretical and began showing up in markets.

AI is compressing the lifespan of every corporate advantage, and the market is already pricing it: in July, volatility inside the equity market hit all-time highs while Bitcoin’s volatility sat at cycle lows, and this paper argues those two facts are the same story.

This is a long paper because the argument required a market event before it could be seen clearly. July supplied it, so pour the coffee and settle in.

Two volatility readings crossed in July. Factor volatility, the turbulence inside the equity market’s most crowded AI trades, exploded to all-time highs. Bitcoin’s realized volatility spent the same month at cycle lows, absorbing a double-digit drawdown that in any prior cycle would have left it vulnerable to risk-asset weakness and sent its volatility past 80%. Winners versus losers in technology risk assets saw their volatility rise to levels higher than during the dot-com bubble and the GFC, while Bitcoin volatility did not move and the asset finished higher for the month. The asset built on forecastable cash flows turned violent while the asset with no cash flows went quiet. This paper is my attempt to explain why.

The short version: AI is compressing investment time. Products get built faster, competitors arrive sooner, and the duration of every moat, the input no DCF prices carefully, is shrinking. The market’s doubt has climbed the entire stack in a year: software companies, then the model labs, and now the hyperscalers, questioned at the very moment they carry roughly $1.7 trillion in forward demand and their customers reserve capacity years in advance. Kimi K3 showed frontier capability now spreads in days, so even historic revenue growth at Anthropic and OpenAI buys no immunity. Jevons Paradox says cheaper intelligence explodes consumption. What I call the Intelligence Competition Paradox says it melts ownership. Both are true at once.

That leads to double debasement: fiat printing debases the money, while AI printing debases the moat. The paper ends with a four-layer investment map: own the physical bottlenecks, own the distribution layer that wraps intelligence in trust, re-underwrite every moat against abundant cognition, and hold exposure to scarcity that no press can reach, because liquidity is an option on time.

No company is safe. The volatility market figured that out first.

AI Is Compressing Investment Time and Forcing a New Search for Value

Two volatility readings crossed in July, and the crossing is the strangest fact in markets right now.

The first reading came from inside the equity market. Momentum suffered one of the most violent reversals in the history of factor data, and factor volatility, the turbulence hiding beneath the calm indexes, exploded to all-time highs. The epicenter was the AI trade, the most crowded and most analyzed set of positions in the world.

The second reading came from the asset institutional investors were taught to dismiss as too wild to own. Bitcoin’s realized volatility spent the same month sitting at cycle lows. Its 365-day realized volatility ended July at 37%, close to multi-year lows, while absorbing a double-digit drawdown that in any prior cycle would have sent volatility screaming past 60%. Through July’s storm, it barely stirred while technology momentum factor volatility soared past 100.

Hold those two facts side by side. The asset class built on forecastable cash flows turned violent. The asset with no cash flows at all went quiet. Volatility is the market’s live estimate of uncertainty, and the market just told us it is becoming less certain about the most studied companies on earth and more certain about the asset it spent fifteen years calling a casino.

Markets do not produce a crossover like that by accident. Something deep is being repriced on both sides, and this paper is my attempt to name it.

Start with the equity side of the cross. The hyperscalers reported some of the strongest revenue and backlog numbers ever printed by public companies. Anthropic and OpenAI posted growth curves that enterprise software has never seen. And the market’s response was to question all of them. Not the laggards. The winners.

There is a pattern here, and it has been moving up the AI stack for more than a year. Software companies were the first to be re-underwritten as investors questioned the durability of their products and terminal values. Then it spread to any sector whenever Anthropic released a new tool. The anxiety then reached the hyperscalers, where declining free cash flow, unprecedented CapEx, and rising CDS yields raised concerns about the cost of maintaining AI leadership. It has now reached OpenAI and Anthropic: even historic ARR growth offers limited comfort when rapidly improving open-source models can challenge the duration of their advantage. Every layer once viewed as protected is now being forced through the same re-underwriting process.

I think the market is telling us something it doesn’t yet have words for: AI is compressing economic time. Products get built faster. Competitors arrive sooner. AI-native companies are growing with fewer employees. Advantages that took a decade to construct can be pressured in a quarter. When the clock speeds up, the confidence interval around every terminal value widens, and investors start asking a question that has nothing to do with next quarter’s earnings.

When intelligence becomes abundant, what remains scarce, liquid, and believed in?

That question is where this paper ends, and it is where the volatility crossover finally gets resolved. The journey starts in July, with the unwind.

July Was a Warning About Economic Time

Every violent market episode teaches one lesson if you’re willing to look past the price action. July’s lesson was about duration mismatch.

The investors caught in the unwind were not wrong about AI. Many of them will eventually be proven right. They were wrong about time. They held views that resolve over years inside portfolios that get marked every day, margined every week, and redeemed every quarter. The Situational Awareness episode was the cleanest example: a thesis about the trajectory of machine intelligence, funded by capital with the patience of a mayfly.

When factor volatility finally exploded, hitting levels we have never recorded, the long-term view offered zero protection. Leverage converted uncertainty into forced selling, and forced selling converted a positioning event into a narrative event. That second conversion is the dangerous one.

Here is how it works. Prices fall first. Then investors go looking for a story that fits the tape, and the AI bear case is a fully stocked shelf: circular financing, runaway CapEx, missing ROIC, chip obsolescence, open-source erosion, power delays, vanishing free cash flow. Pick any two. The correction becomes proof of the fear, even when the honest explanation is that too many people owned the same thing with borrowed money.

I traded through 1998 and the LTCM unwind. I watched brilliant long-term theses die of short-term causes. July was that movie again, updated for the AI era, and it previewed the regime we now live in: a technology compounding exponentially, held by humans who think linearly, funded by capital that needs liquidity daily. That collision will happen again. The unwind wasn’t the story. It was the trailer.

AI Is Compressing Investment Time

The human brain is a linear extrapolation machine. We take the last few years, draw the line forward, and call it a forecast. Every DCF model on every desk is a monument to this habit: growth fades gently, margins mean-revert politely, and the moat erodes on a civilized schedule measured in decades.

AI does not respect the schedule.

Model capability now improves in months and soon in days. The price of a unit of intelligence falls in quarters. An open-weight release crosses the planet in a weekend. A five-person team can ship a product, find customers, and attack an incumbent’s margins before that incumbent finishes its annual planning cycle. The gap between invention and imitation, which is the gap where all excess returns live, is closing in front of us.

This is what I mean by the compression of investment time. Nothing about a DCF breaks mathematically. What breaks is the input nobody prices carefully: the duration of the moat. A company can beat every quarter and still be repriced brutally, because the value was never in the next eight quarters. It was in years eleven through thirty, and those years just got harder to underwrite.

So the process has to change. My father taught me to handicap rather than predict, and I have never needed that lesson more than now. Investing in this environment requires Bayesian discipline. You begin with a distribution of outcomes and update it as the evidence changes. Every model release, backlog number, and pricing change moves the probabilities. Positioning is evidence too: when other investors begin bragging about owning the same trade, the fundamental outlook may be unchanged, but the odds embedded in the price have shifted. A view that remains fixed through changing evidence and increasingly crowded positioning has hardened into a story.

Stories are how July happened. Distributions are how you survive the next one.

The Hyperscaler Anxiety and the Math Investors Are Missing

For fifteen years, owning Microsoft, Alphabet, Amazon, and Meta was the closest thing public markets offered to a free lunch. Their scale was the safety. Now the same scale is the anxiety, because staying in the AI race requires spending at a magnitude with no precedent in corporate history.

The bear case is not stupid. These companies are converting oceans of operating cash flow into chips, memory, land, steel, and gigawatts, and the honest underwriting question is whether that capital becomes productive capacity or a very expensive museum of 2026-era silicon. Fear of the second outcome is why the stocks trade the way they do. Nobody, including them, knows what the future holds once we hit AGI, ASI, and a world of humanoids with superintelligence.

But look at what the fear is ignoring today. This earnings season, the demand side of the ledger did not wobble. It accelerated.

Microsoft: $90 billion in quarterly revenue, up 18%, with Azure growing 43% and commercial remaining performance obligations reaching $678 billion, up 84%, and still up 25% with OpenAI stripped out. Alphabet: revenue of $119.8 billion, up 24%, with Google Cloud up 82% and its operating margin expanding from 20.7% to 35.6%; management flagged that growth accelerated meaningfully even excluding TPU system sales, and the cloud backlog hit $514 billion, with just over half converting inside 24 months. Amazon: $200.6 billion in revenue, AWS up 37% for its fastest growth in 18 quarters, and an AWS backlog that jumped from $364 billion to $496 billion in a single quarter, with most 2027 capacity already reserved and commitments reaching into 2028. Meta: revenue up 28%, advertising up 27%, impressions up 14%, and price per ad up 12%, with its compute pointed inward at an ecosystem management believes is generating attractive returns today.

Add it up and Microsoft, Alphabet, and Amazon alone are carrying roughly $1.7 trillion of contracted forward demand. The definitions differ and the conversion timing differs, but the direction does not. Customers are reserving intelligence capacity years in advance, the way airlines reserve aircraft.

Andy Jassy then did something CEOs rarely do: he showed the math. A data center takes about two years of investment before it opens and then earns for roughly 30 years. The AI servers inside it pay for themselves in under three years and keep producing profit for two to three more. Be precise about what that means, because the bears won’t be: the building and the silicon are two different underwriting problems. The shell is a 30-year asset; the servers are five-to-six-year assets on a refresh treadmill. Jassy’s claim covers the harder problem, the silicon, and his answer is that it pays back before it depreciates. And even after lifting 2026 CapEx to $220 billion, he says AWS still cannot build fast enough for the demand it can see.

That is the tension defining this market. As an equal-weight group, Meta, Microsoft, Amazon, and Google finished July up only 3% YTD. The most successful companies in history are being questioned at the exact moment their customers are demanding more capacity. Concerns about declining free cash flow, rising CDS yields, and uncertain returns on invested capital have come to dominate the narrative, even as revenue accelerates, backlogs expand, and management teams describe demand running ahead of supply. July’s unwind changed the positioning and therefore changed the odds: the fundamental risks remain, but the price and crowding around those risks have shifted. The spending may become the deepest moat ever dug or an entry fee that keeps rising. The uncertainty is the truth.

The First Scarcity Trade: Compute

Strip away the noise and the AI economy reduces to one imbalance: intelligence demand compounds at the speed of software, and intelligence supply arrives at the speed of construction.

Demand first. An AI agent is a worker that never sleeps, never unionizes, and spawns copies of itself. It writes code, tests the code, researches the market, drafts the memo, answers the customer, and calls other agents to do the parts it can’t. Every capability improvement expands the set of tasks worth automating, and every newly automated task is a permanent new stream of inference demand. The demand curve doesn’t shift right. It shifts right and steepens.

Supply, meanwhile, is hostage to the physical world: fab cycles, transformer lead times, interconnection queues, permits, concrete, and the finite number of electricians in North America. You cannot download a substation.

This is where the speed of code meets the speed of steel, and the collision creates the first scarcity trade of the AI era. Compute, memory, networking, optics, generation, transmission, cooling, electrical gear, data-center shells. Everything on the steel side of the collision gets more valuable as everything on the code side gets cheaper.

There is a beautiful recursion buried here. The bottleneck’s eventual solution is the bottleneck itself: agents will one day compress data-center design, grid engineering, and permitting, but building those agents requires the very compute we don’t have enough of. Scarcity is funding the tool that ends the scarcity, which tells you the shortage resolves eventually and also tells you it doesn’t resolve soon. 2026 has been the year investors embraced scarcity.

Kimi K3 and the Arrival of Intelligence Abundance

Every regime has a moment when the future stops being theoretical. For intelligence abundance, that moment was Moonshot releasing Kimi K3: 2.8 trillion parameters, open weights, frontier-level coding and agentic capability, free to download. The scarcest input of the new economy was suddenly being given away. This may be the most important connection to Bitcoin as an asset.

Two consequences followed, pointing in opposite directions.

For infrastructure, K3 was rocket fuel. Cheaper intelligence means more viable use cases, more use cases mean more inference, and more inference means more of everything physical. The open-source release that terrified equity investors was, mechanically, a demand shock for compute.

For ownership, it was a grenade. Anthropic had just put up numbers that should have ended every argument: a run-rate near $9 billion at year-end 2025 growing past $47 billion by May 2026. OpenAI was compounding from $20 billion into the mid-twenties. These are the fastest commercial ramps in the history of enterprise technology. And within days of K3, the market’s question was not “How big can this get?” but “How long can they charge for what China now gives away?”

Read that carefully, because it is the whole thesis in one sentence: the biggest revenue winners of the AI era got the same treatment as the hyperscalers. Growth bought them no immunity. Only duration matters now, and duration is exactly what nobody can prove.

My handicapping: the open-source threat is real but aimed at the wrong target. K3 competes for AI-native startups and technical teams that can run their own stack. The Fortune 500 does not want weights. It wants a product: permissions, governance, audit trails, uptime guarantees, support contracts, indemnification, and an interface a compliance officer can love. Anthropic and OpenAI are building that wrapper as quickly as they build models, and $70 billion of combined run-rate says enterprises are paying for the package, not the parameters.

But enterprise preference for a managed product does not make incumbents themselves safe from disruption. In many ways, the speed of progress makes the enterprise adoption problem harder. A startup can choose a model, redesign its workflow, discard the architecture six months later, and begin again. A public company has customers, regulators, boards, legacy systems, cybersecurity obligations, procurement processes, and reputational risk. Every decision must survive committees that know the technology may be obsolete before the implementation is complete. The rational fear of choosing the wrong model, architecture, or vendor can freeze the organization into choosing nothing at all.

That hesitation creates its own risk. While established companies debate whether to build, buy, fine-tune, use open source, or commit to a closed platform, AI-native competitors are building their companies around the assumption that intelligence is abundant, software is disposable, and workflows can be redesigned continuously. The incumbent is trying to attach AI to an existing organization. The startup is designing the organization around AI. In a technology cycle moving this quickly, governance protects the enterprise, but excessive caution can become an accelerant for disruption. The same inertia can also drive the most ambitious employees toward companies where they can build without waiting for institutional permission.

This is not merely an operating problem. These enterprises are also stocks and assets held throughout people’s portfolios, retirement accounts, pensions, and index funds. Many of them have been among the most successful investments of the past fifteen years, and their past consistency has encouraged investors to treat future cash flows as unusually durable. But AI introduces uncertainty precisely where traditional valuation models are most sensitive: the terminal value.

A discounted cash-flow model can accommodate slower growth, temporary margin pressure, or higher capital spending. It becomes far less reliable when the competitive structure of an industry may be rewritten before the forecast period ends. If an incumbent delays too long, chooses the wrong architecture, becomes dependent on a vendor, or loses its economic advantage to an AI-native competitor, the problem is not simply that next year’s earnings estimate is too high. The duration and defensibility of the entire future cash-flow stream may have been misjudged.

That is why AI can create multiple compression even when current earnings remain strong. Investors are not necessarily questioning what these companies earn today. They are questioning how confidently anyone can capitalize those earnings ten or twenty years into the future. The companies that dominated the last fifteen years may still dominate the next fifteen, but the probability distribution is wider, and a wider distribution around terminal value should command a lower valuation multiple.

Still, K3 moved my distribution, and it should move yours. It proved frontier capability now diffuses in days, not years. The labs can keep growing at historic rates while the market rationally shortens the duration it will pay for. Explosive demand, uncertain ownership. Strong current cash flows, unstable terminal values. Hold both. That paradox runs through the rest of this paper.

Jevons Expands Demand. Competition Compresses Ownership.

The AI economy runs on two paradoxes, and confusing them is the most expensive category error in markets today.

The first is old. Jevons observed that making coal-fired engines more efficient increased coal consumption, because efficiency made steam power economical for uses that never justified it before. Swap coal for intelligence: every collapse in the price of a token expands the universe of tasks worth throwing tokens at. Cheaper intelligence, more agents. More agents, more software, more research, more analysis, more automated decisions. This is why open-source model releases are bullish for the physical layer. Jevons measures consumption, and consumption is going vertical.

The second paradox is new, and I’ve named it the Intelligence Competition Paradox. It measures the other side of the ledger: ownership.

Think about what a moat actually was. Every durable business began as an idea, an insight, and what protected the insight was the difficulty of execution: assembling talent, accumulating institutional knowledge, and building at scale, all of which took years. That difficulty was the moat. Scarcity of execution capacity is what allowed insights to compound into decades of excess returns.

Abundant intelligence hands execution capacity to everyone simultaneously. The five-person startup gets the output of five hundred. The incumbent deploys the same capability across existing distribution. The window between your insight and your competitor’s copy of it shrinks from years toward quarters. The economy creates more total value than ever, while the rents attached to any single company become shorter-lived and harder to defend.

One paradox says the pie explodes. The other says every slice melts faster. Both are true at once, which is why intelligence abundance can be simultaneously the most bullish force in infrastructure and the most bearish force in long-duration equity valuation.

Now apply this lens to the arrangement everyone loves to hate: clouds investing in labs, labs committing spend back to clouds, clouds building against those commitments. The consensus sneer is “circular financing.” Maybe. But run it through the competition paradox and it looks different: in a world where advantage decays fast, locking up scarce compute, capital, power, and enterprise distribution inside one integrated loop is exactly how you slow the decay. As long as independent customers keep paying real money at the edge of the circle, and $1.7 trillion of backlog says they are, the circle isn’t a scheme. It’s a fortress. The companies in real trouble are the ones outside it: needing frontier intelligence and scarce compute, owning neither, and lacking the balance sheet to buy their way in.

In the age of abundance, spending at a scale competitors cannot match may be the last scarcity you can manufacture.

No Company Is Safe: The Great Re-Underwriting of Terminal Value

Watch where the market’s doubt has traveled, because the route is the message.

It started at the application layer. Legal platforms, creative suites, workflow software. Their earnings were fine. Their customers stayed. Their stocks were cut anyway, because investors quietly rewrote one assumption: how long the excess returns last. In a DCF, the first five years can be carved in stone while the terminal value, where most of the worth of any growth company actually lives, gets marked down by half. That’s how a company beats the quarter and loses a third of its value in the same month. The market wasn’t repricing the earnings. It was repricing the tail.

Then the doubt climbed to the model layer, and K3 gave it the ammunition: the fastest-growing companies in enterprise history, Anthropic and OpenAI, were suddenly assigned shorter durations on their premium pricing.

And in July it reached the summit. Microsoft, Alphabet, Amazon, and Meta possess unmatched cash flows, distribution, data, and infrastructure. Yet even they are being forced to prove that the capital required to remain on top can earn its cost, while investors push their CDS yields higher.

Software questioned. Models questioned. Hyperscalers questioned. Three layers, one mechanism. The market has lost confidence in its own ability to see moats three years out, and when the range of outcomes widens, the effective discount rate on distant cash flows rises for everyone. That is multiple compression, and it can hit a business whose fundamentals never miss a beat. It is also the equity half of July’s volatility crossover, fully explained: factor volatility at all-time highs is simply this chapter, priced in real time. The other half of the cross takes two more steps.

But re-underwriting is not the same as condemnation, and this is where the real work begins. Some moats get stronger in an intelligence flood. Physical assets. Power access. Regulatory licenses. Proprietary data. Networks, brands, and customer relationships so embedded that switching costs survive any model release. The screen for every equity you own now reduces to a single question, and I’d suggest writing it at the top of every research file:

How much of this company’s advantage depends on intelligence staying scarce, and how fast can a well-funded competitor armed with abundant intelligence close the gap?

If you can’t answer that, you don’t own a thesis. You own a ticker.

The Double Debasement of Capital

Schumpeter gave capitalism its honest job description: creative destruction. New firms carrying new technology dismantle old firms, and capital migrates to its most productive use. For a century, the process ran slowly enough that investors could domesticate it. Own a diversified basket of equities and destruction becomes rotation; the index quietly swaps the dying for the emerging, and your wealth rides the aggregate.

That bargain rested on a hidden assumption: destruction proceeds at human speed. AI breaks the assumption. When execution capacity becomes abundant, Schumpeter’s gale stops being seasonal weather and becomes the climate.

Now stack this on top of the older, more familiar erosion. Monetary debasement is the on-ramp most investors already understand. Writers like Natalie Brunell, through her recent book Bitcoin Is for Everyone: Why Our Financial System Is Broken and Bitcoin Is the Solution, have helped introduce newcomers to the argument: when money is created faster than the real economy grows, cash bleeds purchasing power, and savers flee into hard assets and equities. For decades, equities were the escape. Owning productive businesses was how you outran the printer.

Here is the uncomfortable arrival point: the escape vehicle is now being debased too.

Fiat printing debases the money. AI printing debases the moat.

Two presses running at once. One dilutes the unit you measure wealth in. The other dilutes the durability of the corporate claims you bought to protect that wealth. Cash melts slowly; moats now melt quickly; the index still works in aggregate but forces you to live through the compression and carnage in between. This is double debasement, and it is a genuinely new problem in capital allocation.

One investor met this problem early, and markets called him crazy and still do. In 2020, Michael Saylor ran MicroStrategy, a profitable, cash-rich, mid-sized software firm in a world ruled by Microsoft, Alphabet, Apple, and Amazon. He faced both blades at once: the Fed had taken rates to zero, guaranteeing his cash would rot, and he was honest enough to admit his software moat could never out-compound the platforms. Trapped between the printing of money and the printing of competition, he went searching for an asset that neither press could touch. He landed on Bitcoin, and the market spent years treating him as a punchline. His line, “You don’t find Bitcoin, Bitcoin finds you,” captures the process.

I’d put it differently. Saylor wasn’t early to a trade. He was early to a diagnosis, and the disease has now reached every company on the board.

The Return of the Store of Value

Every store of value is an insurance policy against a named threat. Gold insures against sovereign monetary disorder. Real estate insures against inflation with physical scarcity. Equities insure against stagnation by claiming tomorrow’s earnings. You choose your store by choosing which threat you fear.

Intelligence abundance adds a threat the old policies weren’t written for: the corporate rent itself, the excess return that makes an equity claim worth holding, is becoming shorter-lived and harder to forecast. Against that threat, the asset menu gets re-scored. Investors will still own great businesses and the physical bottlenecks of the buildout. But a rising premium should flow toward assets whose scarcity does not depend on any technology staying ahead or any management team staying sharp. Scarcity that survives regime change.

And one property jumps the queue in this environment: liquidity.

Liquidity is an option on time.

When the economic map is being redrawn this fast, the ability to wait, reassess, and move is itself an asset. Illiquidity is a short position in optionality precisely when optionality is most valuable. The faster advantages decay, the more you should pay for the right to change your mind.

But scarcity and liquidity alone don’t make a store of value. The third, and most important, ingredient is belief. Gold’s scarcity mattered only because a hundred generations trusted that the next holder would honor it. A scarce object with a small belief network is a collectible. A scarce object with a deep, durable, self-reinforcing belief network is money.

Bitcoin has already passed the belief test. It grew up and survived while investors still had abundant choices among dominant companies with deep moats, durable cash flows, and seemingly predictable futures, yet its ownership continued to spread across countries, generations, and institutions. In a world where AI makes those moats less durable and those futures harder to value, Bitcoin may become more attractive precisely because its scarcity does not depend on preserving a corporate advantage, only on the continued belief of a global network that has already survived every reason to abandon it.

Watch what builds belief: anxiety. When wages, careers, savings, and now corporate ownership all feel less able to carry today’s effort into tomorrow’s security, people hunt for a new anchor, and they hunt hardest during transitions. AI is engineering exactly that psychology at scale: more productivity and more abundance, alongside less predictable labor income, less durable career paths, and less trustworthy terminal values. Abundance and anxiety, rising together. That combination has always, eventually, minted a new store of value.

Bitcoin as an Option on Time

Follow the argument to its destination and you arrive at an asset most institutional readers would rather not discuss. Discuss it anyway, because it is the only candidate purpose-built for both presses.

Bitcoin’s supply schedule answers to no CEO, no board, no moat, and no finance ministry. Twenty-one million, enforced by code and consensus. Print all the fiat you want; the schedule doesn’t move. More importantly for this paper: make intelligence as abundant as air, and the schedule still doesn’t move. A smarter model cannot code more Bitcoin into existence. It may be the only major liquid asset whose issuance scarcity is structurally indifferent to both monetary policy and machine intelligence.

Around that scarcity sit the other two ingredients. Liquidity: global, around-the-clock, borderless, giving the holder the mobility this regime rewards. Belief: sixteen years of survival through crashes, bans, and obituaries has compounded into a network of holders who expect future holders to recognize the same properties. Scarcity, liquidity, belief. The full recipe.

For years, the honest caveat was that Bitcoin trades as a liquidity-sensitive risk asset and is viciously volatile, and I have written that caveat more times than I can count. The caveat is aging, and the tape says so. One-year realized volatility ended the second quarter near 42%, at multi-year lows, while the asset absorbed a 14% drawdown of the kind that used to send volatility above 80%. Through July, as factor volatility inside the equity market printed all-time highs, Bitcoin’s realized volatility sat at cycle lows. It has recently been less volatile than dozens of S&P 500 constituents, and its worst single day in 2025 was milder than Tesla’s or Nvidia’s.

This is the crossover from the opening of this paper, and here is its resolution. The volatility market is beginning to price the possibility that uncertainty is migrating from the asset with no cash flows to assets whose cash flows are becoming harder to forecast. Equity volatility is rising because the central question about companies—how long does the moat last?—gets harder every quarter. Bitcoin’s volatility is falling because its central question—will the belief network hold?—has spent sixteen years being answered. One asset’s uncertainty is compounding. The other’s is resolving. The options market noticed before the asset allocators did.

Discipline still applies, so state the hedges plainly. Low-volatility regimes end. Compression often precedes expansion. Bitcoin has not yet been tested by a true liquidity crisis in its ETF era, and I assign real probability that its next stress episode looks more like a risk asset than a haven. The claim is not that Bitcoin protects you from drawdowns. The claim is that it preserves your option on time—your mobile, unprintable, undisruptable claim on the future—while the duration of nearly every other claim gets marked down, and the volatility market has begun pricing exactly that.

There is also a second act most analyses miss. Humans adopt money through narrative; we needed gold to be shiny and storied. Agents don’t. An AI agent evaluating treasury or collateral assets runs a scorecard: supply credibility, settlement finality, liquidity depth, portability, counterparty risk, censorship resistance, behavior across regimes. Human adoption is narrative-first. Agent adoption will be scorecard-first, and Bitcoin was practically designed to ace the scorecard. Gold cannot settle at machine speed without a custodian standing in the middle; Bitcoin can. As agents take over growing shares of treasury management, collateral selection, and cross-border settlement, with stablecoins as the transaction rail and Bitcoin as the non-sovereign reserve beneath it, the marginal buyer of monetary properties may stop being a person at all.

So Bitcoin enters the AI story through the deep door, not the shallow one. Not because miners buy chips. Because Bitcoin may be the purest AI trade.

On one side, it is unusually insulated from intelligence abundance. AI can replicate software, compress margins, accelerate competition, weaken corporate moats, and make terminal values harder to forecast. It cannot increase Bitcoin’s supply, rewrite its issuance schedule, or create a faster-moving competitor that produces more Bitcoin. The same force that makes intelligence less scarce leaves Bitcoin’s monetary scarcity untouched.

On the other side, Bitcoin may benefit directly from the transition from human financial judgment to machine financial judgment. Humans choose assets through stories, familiarity, institutional habit, and emotion. Agents will increasingly choose through rules and scorecards: verifiable scarcity, liquidity, portability, settlement speed, counterparty risk, censorship resistance, and independence from any single government or corporation. Bitcoin is not merely compatible with that framework. It is native to it.

AI agents will also require a new world of financial guardrails. Autonomous systems cannot be given unlimited discretion over money. They will need programmable limits, transparent collateral, auditable settlement, hard risk constraints, and assets whose rules cannot be quietly changed by management teams or intermediaries. Stablecoins may become the transactional rail, but Bitcoin is a natural candidate for the scarce, non-sovereign reserve asset beneath that system.

That is what makes Bitcoin the purest AI trade. It is not harmed by the abundance AI creates, and it may benefit from the financial architecture AI requires. Intelligence can disrupt nearly every asset built on human judgment, corporate execution, or institutional trust. Bitcoin is an asset intelligence cannot dilute and machines may increasingly prefer.

The New Investment Map

Pull the threads together and you don’t get a trade. You get a hierarchy, ordered by time horizon, and I’ll attach my probabilities so this stays a distribution rather than a story.

The first layer is physical, and it’s now. Compute, memory, optics, networking, power, cooling, electrical infrastructure, data-center capacity. Demand compounds at software speed; supply arrives at construction speed; the spread accrues to whoever sells the scarce inputs. This quarter’s backlogs and capacity reservations pushed my odds that the buildout runs constrained through at least 2028 higher still. The CapEx receivers remain my core expression: paid on the duration of the build, without the spenders’ depreciation debate.

The second layer is distribution, and it’s next. As models commoditize, value migrates to whoever wraps intelligence in trust: security, governance, workflow, support, and interfaces the Fortune 500 can deploy. Anthropic, OpenAI, and the hyperscalers are racing to own that wrapper. Grade them ruthlessly on retention, margins, and the stickiness of distribution, not on benchmark scores.

The third layer is the re-underwriting, and it never ends. Every equity gets screened against the question: how much of this moat is really rented from the scarcity of intelligence? Physical assets, licenses, proprietary data, networks, brands, and embedded relationships pass. Moats made mostly of accumulated cognition fail. On the five-year horizon, I put roughly 60% odds that AI rents relocate to these new scarce layers rather than dissipating broadly, which means equity ownership survives but the map of what’s ownable gets redrawn. The other 40% is the world where rents dissipate faster than they relocate, and that scenario is what the final layer insures.

The final layer is monetary, and it’s the destination. Double debasement, the printing of money and the printing of competition, drives capital toward scarcity that survives both. Liquidity for mobility. Bitcoin for the scarcity no press can reach.

July showed us the whole map in miniature. The unwind showed long-duration conviction dying of short-term liquidity. The hyperscaler prints showed record demand coexisting with record doubt. Kimi K3 showed the disruptors getting disrupted. And the tape kept score the whole way: factor volatility at all-time highs, Bitcoin volatility at cycle lows. The crossover that opened this paper is the market’s own one-line summary of everything in between.

AI is printing intelligence, and that intelligence is printing competition.

The result is an economy that will create more value than any in history while making it harder than ever to know who keeps it. Own the bottlenecks while they bind. Own the moats that abundance cannot dissolve. Stay liquid, because liquidity is an option on time. And hold something whose scarcity outlives the regime, because the regime is changing faster than the models on our desks assume.

No company is safe.

That is not a counsel of despair. It is the starting gun for the most important search in markets today: what remains scarce, liquid, and believed in when intelligence itself becomes free?

I always enjoy speaking with someone new about AI and crypto, but this conversation was especially compelling because it focused so directly on the intersection between the two. Most investors, technologists, and commentators still place them in separate silos. AI is discussed as a revolution in intelligence and productivity, while crypto is treated as a parallel story about money, markets, and digital assets.

That separation misses the larger transformation.

AI is creating a new population of autonomous economic actors. Crypto is building the financial infrastructure those actors will need to transact, establish ownership, verify identity, and operate within enforceable limits. The more capable agents become, the more inseparable these two technologies will appear.

Whenever I speak with someone who has spent years thinking deeply about that convergence, I leave with new questions, new ideas, and a clearer view of where the world may be heading. There is something unique about a conversation between two people trying to understand the future. When you talk about the past, you are examining a landscape that has already been mapped. The facts are known, the outcomes have occurred, and the story has largely been written.

Talking seriously about the future feels more like exploring a new land. You are searching for connections, testing assumptions, and trying to describe a world that does not fully exist yet.

My conversation with Mark led directly to this post because it reinforced a belief that has been growing stronger for me each day a new AI model is released: we are standing on the doorstep of a change the world has never experienced.

A new digital species is about to enter the economy.

For most of modern economic history, investors have evaluated demand through a human lens. More consumers purchase more goods, more businesses hire more employees, and more factories produce more output. Demand expands broadly with population, income, and GDP, creating a largely linear relationship between economic growth and resource consumption.

Artificial intelligence introduces a fundamentally different demand curve because the economic unit of production is beginning to shift from humans to software agents. Yet many investors continue to evaluate AI infrastructure through the framework of traditional enterprise technology. They see Microsoft, Amazon, and Google investing hundreds of billions of dollars in data centers while OpenAI, Anthropic, xAI, and a handful of frontier laboratories consume enormous amounts of compute. From that perspective, AI demand appears concentrated among only a few customers.

That framing overlooks the structural transition underway. Frontier model companies are evolving into platforms that deploy and orchestrate billions of autonomous software agents. The real source of demand is the digital workforce they enable. The cost of that digital workforce are tokens and the cost of tokens per capability is declining rapidly and the competition is rising rapidly.

It also overlooks the financial system this workforce will require.

Billions of digital agents cannot operate at scale through a financial system designed around human banking hours, manual approvals, delayed settlement, fragmented databases, and transactions that assume a person is sitting on the other side of every payment. Agents will need the ability to hold value, exchange value, verify counterparties, enforce spending limits, execute contracts, and settle transactions continuously.

Crypto provides the financial guardrails for that world.

We Are Measuring the Wrong Customer

Every quarter, investors ask the same question after hyperscaler earnings and their growing RPOs: Who is buying all of this capacity?

Viewed through a traditional enterprise lens, the answer appears straightforward. OpenAI rents Azure, Anthropic builds on AWS, Google operates its own infrastructure, and Meta trains its own models. If those companies represent the end market, AI infrastructure spending appears concentrated and potentially vulnerable to the spending decisions of only a few organizations.

The economics change once agents become the unit of demand. OpenAI, Anthropic, and Google increasingly resemble operating systems for digital labor rather than end users of compute. Every enterprise application, software workflow, and consumer service built on those platforms creates incremental inference demand. The hyperscalers are therefore building infrastructure for an installed base of software workers that may eventually number in the billions.

The same measurement problem applies to financial activity. A bank or payment network may initially see one large AI platform, enterprise, or digital wallet as the customer. Beneath that account could sit millions of agents making continuous economic decisions on behalf of individuals, companies, vehicles, robots, and other agents.

Counting the corporate account misses the economic activity occurring underneath it.

One Customer, Billions Served

This measurement illusion is surprisingly familiar.

McDonald’s appears to its suppliers as a single customer, yet its famous “Billions Served” campaign captured the economic reality beneath the corporate entity. One company represented billions of individual customer transactions occurring every year.

The AI economy is beginning to exhibit the same dynamic at a far greater scale. OpenAI may appear as a single Azure customer, Anthropic as a single AWS customer, and Google as a single internal cloud consumer. Beneath each platform sits an expanding population of software agents performing research, writing code, analyzing data, serving customers, generating content, executing financial workflows, and coordinating with other agents.

Each agent represents recurring demand for inference, and each task may require dozens of model calls across reasoning, retrieval, search, coding, memory, and planning systems. From the cloud provider’s perspective, one platform customer increasingly represents billions of software workers operating continuously.

The financial system will experience the same explosion.

A single consumer agent could compare prices across hundreds of vendors, negotiate subscription terms, rebalance savings, purchase compute, pay for data, compensate specialized agents, and settle dozens of microtransactions before the user wakes up. An enterprise agent could purchase inventory, rent processing capacity, manage working capital, hedge foreign exchange exposure, and pay other agents for completed tasks.

One human instruction could generate hundreds or thousands of underlying economic transactions.

The cloud providers are building infrastructure for billions of digital workers that have yet to be deployed. Crypto networks are building the transaction, settlement, identity, and ownership infrastructure those workers will need once they begin conducting business with one another.

Intelligence Needs Financial Agency

The first generation of AI systems primarily produced information. They answered questions, summarized documents, generated images, wrote code, and helped people make decisions.

Agents move from producing information to taking action.

Once an agent can act, it needs financial agency. It must be able to purchase resources, compensate service providers, receive revenue, manage budgets, and verify that contractual conditions have been met. An agent that can recommend a transaction but cannot execute it remains a sophisticated assistant. An agent that can securely control economic resources becomes an economic participant.

Traditional financial infrastructure was built around identifiable humans and incorporated layers of human friction as a form of security. People sign documents, enter passwords, wait for banks to open, approve transfers, reconcile accounts, and resolve disputes through centralized institutions. Those processes are cumbersome, but the delays are manageable when humans initiate a limited number of transactions.

They become an impossible bottleneck when software agents operate continuously and transact at machine speed.

The number of transactions generated by billions of agents is beyond normal human comprehension. Agents may pay fractions of a cent for data, inference, storage, bandwidth, identity verification, API access, intellectual property, or another agent’s specialized output. These transactions may occur thousands of times during a single workflow and millions of times across an enterprise.

A financial system for agents therefore needs to be programmable, continuously available, globally interoperable, auditable, and capable of settling extremely small transactions economically.

This helps explain why Stripe’s reported bid for PayPal matters beyond the immediate takeover price. Stripe has built much of the merchant-facing infrastructure of internet commerce, while PayPal brings an enormous consumer network, Venmo, Braintree, digital wallets, and its PYUSD stablecoin. Combining those assets could create a financial platform spanning both sides of agentic commerce: the businesses receiving payments and the consumers authorizing agents to make them.

The strategic message is more important than whether this particular transaction is ultimately completed. The payment industry is beginning to prepare for a world in which checkout is initiated by software rather than humans. Stripe is already building transaction-specific credentials that allow agents to make authorized purchases without gaining unrestricted access to a customer’s underlying payment information. PayPal is also investing in the infrastructure and protections required for agent-driven commerce.

The future payment network must do more than move money. It must verify that an agent is authorized, restrict what it can purchase, determine how much it can spend, protect the underlying credentials, assess fraud risk, and preserve a record of who approved the transaction.

Cards and bank accounts may continue to fund many purchases, but the control layer surrounding them must become programmable. Stablecoins, tokenized deposits, programmable wallets, cryptographic identity, and smart contracts provide the architecture needed as commerce moves from occasional human checkout to continuous machine execution.

The Stripe–PayPal bid can therefore be viewed as another sign that the financial industry recognizes what is approaching. AI agents are becoming economic actors, and the companies that control their wallets, permissions, identity, settlement, and access to merchants may control the most important financial gateway of the agentic era.

That architecture increasingly resembles crypto.

Crypto Provides the Guardrails

The term “crypto” often causes investors to focus on speculative tokens and price volatility. The more important long-term function will be the creation of digitally native property rights and financial rules for autonomous software.

Agents need more than a payment rail. They need guardrails determining what they own, what they may spend, which counterparties they may interact with, and what conditions must be satisfied before funds are released.

A programmable wallet can give an agent access to a specific budget without granting it unrestricted access to an individual’s or corporation’s entire balance sheet. Permissions can limit transaction size, merchant category, jurisdiction, asset type, time window, or cumulative spending. Larger transactions can require additional authentication or human approval.

Smart contracts can hold funds in escrow and release them only when verifiable conditions are met. An agent can pay another agent after a task is completed, a shipment is confirmed, a digital service is delivered, or a predefined performance threshold is achieved.

Stablecoins can provide agents with a digitally native medium of exchange whose value remains understandable in conventional economic terms. Tokenized deposits, Treasury securities, money-market instruments, securities, intellectual property, and real-world assets can allow agents to move between cash, collateral, investments, and productive resources without leaving the same programmable environment.

Public and permissioned ledgers can create audit trails showing which agent authorized a transaction, which rules governed it, what assets were transferred, and whether the action remained within its permitted mandate.

These are not decorative features. They are the financial safety system required to prevent autonomous commerce from becoming autonomous chaos.

The Scale Makes Traditional Oversight Impossible

Human financial supervision relies heavily on reviewing exceptions after activity occurs. Compliance departments inspect transactions, accountants reconcile books, auditors sample records, and regulators examine reports produced days, weeks, or months later.

That approach cannot scale to an economy in which billions of agents transact continuously.

Humans will not manually inspect every microtransaction between agents. They will establish policies, permissions, risk limits, and identity requirements that are enforced automatically at the moment of execution. Oversight must become embedded in the transaction itself.

Crypto makes financial rules programmable.

An enterprise can permit a procurement agent to spend up to a predefined amount with approved vendors. A consumer can allow a travel agent to purchase a flight within a price range but require approval before booking a hotel. A portfolio agent can rebalance among approved assets while remaining prohibited from borrowing, using leverage, or interacting with unauthorized protocols.

The ledger then provides a record of every action taken within those boundaries.

This creates a financial hierarchy suited to autonomous systems. Humans define goals and risk limits. Agents optimize within those limits. Cryptographic infrastructure verifies identity, enforces permissions, transfers ownership, and records the result.

Without those guardrails, giving an agent control of money would resemble giving an employee unlimited access to every corporate bank account and hoping internal policies are followed. The potential productivity would be overwhelmed by the security risk.

With programmable financial constraints, economic authority can be delegated in narrow, measurable, and revocable increments.

Stablecoins Become the Native Currency of Agents

Software agents are global by design. They may purchase services from providers in multiple countries, rent compute from decentralized networks, pay for data feeds, and compensate other agents regardless of geography.

The traditional banking system fragments that activity across currencies, correspondent banks, payment processors, operating hours, and jurisdiction-specific infrastructure. A transaction can pass through several intermediaries before reaching its final recipient, with each layer adding delay, cost, and reconciliation requirements.

Stablecoins offer agents a common digital settlement instrument that can move continuously across compatible networks. Their importance in the agent economy comes from their programmability, availability, and ability to function inside software workflows.

An agent does not care about the prestige of a banking relationship or the design of a payment application. It cares about execution speed, reliability, cost, liquidity, and certainty of settlement. It will route transactions toward the most efficient available rail just as software routes internet traffic toward available bandwidth.

As agents become more capable, they may continuously optimize where cash is held, which stablecoin is used, what network provides the lowest cost, and which tokenized instrument offers the best risk-adjusted yield. Financial balances that remain stationary because humans are inattentive may become increasingly mobile when agents constantly evaluate alternatives.

This is one reason the agentic economy may accelerate the movement toward stablecoins and tokenized assets. These instruments are designed to be held, evaluated, transferred, and exchanged by software.

Identity Becomes as Important as Money

For agents to transact safely, the financial system must know more than whether funds are available. It must know who or what is authorized to use them.

An agent may act for an individual, a corporation, a government agency, a vehicle, or another software system. Each relationship requires a verifiable chain of authority. The counterparty must know that the agent is genuine, that it has permission to perform the transaction, and that its credentials have not been revoked.

Cryptographic identity can establish those relationships without requiring every transaction to be manually confirmed. An agent can prove that it represents an approved organization, possesses a specific license, complies with jurisdictional requirements, or has been authorized to spend within a defined mandate.

Reputation may also become portable. Agents that complete work reliably can build verifiable transaction histories. Those records can help other agents evaluate counterparties, price risk, request collateral, or refuse interaction.

The agentic economy will therefore require a combination of identity, money, reputation, permissions, and settlement. Crypto networks are increasingly capable of combining these elements within a common architecture.

Agents Change the Shape of Demand

Traditional software improves worker productivity by helping employees complete tasks more efficiently. Agents increasingly complete the work themselves, allowing organizations to expand their effective workforce without proportionally expanding payroll.

A single employee may supervise dozens of specialized agents responsible for coding, research, legal review, scheduling, customer support, financial analysis, or content generation. Those agents routinely invoke additional specialized models during a workflow, creating multiple layers of inference beneath every user interaction.

One human decision can therefore generate dozens or hundreds of computational events. What appears to be a single prompt increasingly becomes an orchestration layer coordinating reasoning models, search systems, memory, retrieval, translation, image generation, and domain-specific agents. Software begins consuming software, causing compute demand to compound rather than simply grow alongside user adoption.

Financial activity compounds in the same way.

An agent completing a business task may purchase data from one provider, inference from another, storage from a third, and verification from a fourth. Each provider may use its own agents and subcontract additional digital services. A single economic objective branches into a network of machine-to-machine payments.

This is the fundamental difference between human labor and digital labor. Human organizations scale by adding employees. Agentic organizations scale by multiplying intelligence, decisions, and transactions.

Jevons’ Paradox Applies to Transactions

Economic history provides a useful framework for understanding this behavior. Improvements in coal efficiency increased coal consumption rather than reducing it. Lower bandwidth costs dramatically expanded internet usage, while cheaper storage created an explosion in digital information.

Artificial intelligence applies the same principle to cognition. Every reduction in inference costs makes new applications economically viable. Tasks that cannot justify one dollar per inference become attractive at one cent, while another order-of-magnitude reduction allows intelligence to be embedded into products, workflows, and services that previously could not support it.

The same principle applies to financial transactions.

Human beings avoid tiny transactions when fees and friction exceed the value being exchanged. Agents will transact whenever the expected benefit exceeds the marginal cost. As settlement becomes cheaper, agents can pay for increasingly granular units of data, compute, attention, energy, software, and intellectual property.

Lower transaction costs will therefore increase the number of transactions rather than merely make the existing volume cheaper. Machine commerce could ultimately generate transaction counts that dwarf today’s consumer payment system because software can divide economic activity into increments too small and too frequent for humans to manage.

The cheaper intelligence and settlement become, the more agents will consume both.

Compute and Crypto Are Complementary Infrastructure

The AI investment debate often separates compute infrastructure from digital-asset infrastructure. One is treated as productive technology, while the other is treated primarily as a speculative financial market.

The agent economy reveals that they are complementary layers of the same system.

Compute gives agents intelligence. Networks give them communication. Crypto gives them ownership, identity, money, and enforceable economic boundaries.

An agent without compute cannot reason. An agent without connectivity cannot coordinate. An agent without a secure financial architecture cannot become a trusted economic participant.

Data centers, semiconductors, memory, networking, power generation, blockchains, stablecoins, tokenized assets, digital identity, and programmable wallets are therefore components of the same emerging machine economy.

The AI factories will produce intelligence. Crypto rails will allow that intelligence to exchange value.

Investors Are Counting Companies Instead of Agents

Much of Wall Street still measures AI demand by counting frontier model companies, hyperscalers, or enterprise software vendors. That approach is analogous to evaluating the early internet by counting browser developers rather than internet users.

The installed base that matters is the population of software agents. Every enterprise deploying customer-service agents, every developer building coding agents, every healthcare provider implementing diagnostic systems, every financial institution automating research, and every consumer using personal assistants contributes recurring inference demand.

Every successful deployment also creates potential financial activity. Each agent may purchase resources, pay other agents, receive revenue, manage collateral, or move capital among competing opportunities.

Over time, every smartphone owner may interact with multiple personal agents, every enterprise workflow may coordinate hundreds of specialized agents, every autonomous vehicle may maintain its own financial wallet, and every humanoid robot may purchase energy, replacement parts, software, and services.

Infrastructure demand expands alongside the digital workforce. Transaction demand expands alongside its economic activity.

Billions of Agents Require Billions of Guardrails

The debate over whether OpenAI, Anthropic, Google, or Microsoft can individually justify today’s infrastructure spending misses the larger transformation taking place. These companies are becoming distribution platforms for an economy increasingly populated by software workers whose marginal cost continues to decline.

As organizations shift from hiring humans to deploying agents, demand for compute grows in proportion to every opportunity where intelligence can create value. Each reduction in the cost of inference expands the number of agents that can be deployed, the complexity of tasks they can perform, and the volume of computation flowing through the cloud.

The same expansion will occur in finance. Each additional agent creates new payments, contracts, asset transfers, collateral requirements, and machine-to-machine transactions. The volume will quickly surpass the capacity of humans to approve, supervise, reconcile, or even comprehend each individual action.

That is why crypto financial infrastructure is so important. Stablecoins, tokenized assets, programmable wallets, cryptographic identity, smart contracts, and verifiable ledgers provide the guardrails that allow financial authority to be delegated safely to software.

Just as McDonald’s became famous for “Billions Served,” the cloud providers are quietly becoming the infrastructure behind billions of digital workers. Crypto networks may become the financial infrastructure behind the trillions of transactions those workers generate.

AI gives agents intelligence. Crypto gives them economic agency with enforceable boundaries.

You cannot serve billions of digital agents without financial guardrails built for machine speed, and the resulting volume of transactions will be beyond anything the human economy has ever experienced.

Sentiment in crypto is depressed. Even the Bitcoin permabears seem reluctant to keep kicking it while it is down. That is usually when I start paying attention. When sentiment gets this bad, and when bearish narratives have had eight months to harden into consensus, the next rally rarely begins with everyone suddenly finding a new reason to be bullish. It usually starts quietly. The pressure stops getting worse, the marginal seller runs out of ammunition, bad news loses its impact, and price begins to stabilize before the narrative changes. Like watching a tree grow, the turn happens slowly enough that most people do not notice it until it is already visible.

Since the fourth quarter of 2025, one major macro trend has acted as an extreme headwind for Bitcoin: the rise of Opus 4.5 and the beginning of the agentic AI world. That catalyst created three related pressures. First, it accelerated the collapse in software names as investors began discounting AI disruption. Second, it drove a major jump in capex expectations to support the parabolic increase in inference demand from AI agents. Third, it pulled attention, capital, and market leadership toward the physical infrastructure side of AI, including semiconductors, power, cooling, data centers, electrical equipment, and everything needed to build the agentic economy.

The final pressure was the most important for Bitcoin. Higher AI capex numbers, stronger nominal GDP, and the earnings strength of the AI infrastructure complex changed the market’s view of the Fed. Over the last six months, investors moved from expecting multiple rate cuts by the end of 2026 to debating the possibility of multiple rate hikes. Bitcoin became a casualty of the AI agentic rise because the same force that proved AI was real also pulled capital away from crypto and pushed the market toward a more hawkish Fed interpretation.

That leaves Bitcoin with two potential catalysts now. The first is capital rotation. The AI trade has sucked the oxygen out of many parts of the market. For the last two years, exposure to the right infrastructure names, semiconductor names, power and cooling names, or physical-world beneficiaries was rewarded. Software, smaller growth companies, crypto, and anything not directly attached to the AI infrastructure boom were mostly left behind as capital crowded into the dominant earnings and capex story.

That crowding now matters. AI remains the dominant structural trend, but the easy-money phase has passed. Equity momentum is crashing, the infrastructure trade is choppy, and the mid-cycle slowdown is making the same AI beta harder to harvest. Momentum investors still need beta. They do not want to sit in cash forever. As the AI infrastructure trade becomes more volatile, crowded, and difficult to own, they will start looking for a new expression of the same technological regime shift that has not already been fully harvested.

Bitcoin fits that search. It sits outside the crowded AI equity trade while still connecting to the next phase of the digital economy. It can benefit if capital starts looking for a cleaner expression of AI-driven change. It can benefit if investors want exposure to the agentic economy without owning the same crowded infrastructure names. It can benefit if the market begins to realize that AI agents will change software, labor, corporate margins, and eventually the financial architecture required for autonomous economic activity.

The second catalyst is the rate-expectation reversal. Over the last six months, Bitcoin has traded like an asset tied to the disappearance of rate cuts. As cuts were priced out and hikes entered the conversation, Bitcoin moved lower. That does not reduce Bitcoin to a simple rates trade. It means the marginal buyer stepped away as real-rate pressure rose, the dollar narrative improved, and Fed watchers turned the AI infrastructure boom into an argument for a more restrictive central bank.

This is where the art of unlearning the Fed becomes central to the next Bitcoin narrative. In my recent paper, “The Art of Unlearning the Fed,” I argued that the next phase of Fed watching will require something harder than learning a new framework. It will require unlearning an inherited one. For decades, investors listened to the Fed through the same language: hawkish or dovish, restrictive or accommodative, higher for longer or pivot, dots or no dots. That language worked in a more linear economy where policy, inflation, labor markets, and productivity moved with long and variable lags. AI is making that world less stable, less measurable, and more reflexive.

That matters for Bitcoin because the market is still using the old language to price a new economy. Fed watchers are focused on the demand side of AI. They see data centers, chips, energy, cooling, construction, electrical equipment, and grid investment. They see a private-sector capex boom landing on top of 5–6% fiscal deficits, rising debt, and growing interest expense. Inside the inherited framework, that combination looks inflationary. AI capex plus fiscal deficits equals stronger nominal GDP. Stronger nominal GDP equals a hawkish Fed. A hawkish Fed equals higher real rates, a stronger dollar, and lower Bitcoin.

That has been the dominant equation for the last six months. It is also one of the main reasons Bitcoin has struggled. The market has treated AI primarily as a demand shock, but the agentic phase introduces a second force: supply expansion through digital labor. AI agents, copilots, autonomous workflows, coding agents, compliance agents, customer service agents, research agents, and operational agents are the first visible form of digital labor entering the production function. The infrastructure buildout increases demand today, while agent adoption increases potential output tomorrow.

Kevin Warsh has been unusually clear about this. He has described the current moment as “the most disruptive moment in modern economic history in the U.S. and the world,” and later said, “This is a big paradigm shift both for the conduct of our policy and for our economies.” He is treating AI as a force powerful enough to require a new central banking paradigm, which is why his demand-versus-supply framework matters so much for Bitcoin.

Warsh acknowledged that AI infrastructure spending would affect demand and could add “a few tenths of 1%,” but he immediately separated that from the larger supply-side possibility, saying AI’s ability to increase potential output “could be considerably bigger.” That distinction is the whole Bitcoin setup. The market has priced the AI demand impulse first. The next repricing may come from the AI supply impulse.

If the market is wrong about that balance, the cross-asset setup changes. A Fed chair who understands AI may sound tough on inflation credibility while recognizing that productivity is improving. He can defend the inflation target while questioning whether the economy’s speed limit has changed. He can be cautious on rates while understanding that digital labor may weaken wage pressure even as output stays strong. The old labels are too small for the moment because the more important issue is whether AI is rewriting the Fed’s reaction function.

That is why Bitcoin becomes interesting here. The market is crowded into the idea that AI means higher rates. A rally can begin before the Fed turns dovish if investors decide the current hawkish path has been over-discounted. If AI agents begin to mean productivity leverage, disinflationary pressure, labor disruption, and a harder dual-mandate problem, then the rate narrative can shift quickly. Bitcoin only needs rate expectations to stop moving against it before the marginal buyer starts to return.

This is how cross-asset momentum regime shifts usually happen. The market builds a clean consensus, and that consensus becomes a position. In this case, the consensus has been that AI capex creates demand, demand creates inflation, inflation creates hikes, hikes support the dollar, and dollar strength pressures Bitcoin. Then the data and narrative begin to complicate the story. Inflation cools. Wage pressure fails to accelerate. Labor-market internals soften. AI adoption accelerates. Enterprise agents move from pilots to production. Productivity leverage starts showing up in workflows and margins. The simple equation loses its power.

At the same time, the AI equity trade remains in a mid-cycle slowdown. When the AI infrastructure trade was going straight up, it absorbed the speculative oxygen of the market. Now that the trade has become more volatile, crowded, and harder to own, investors will begin looking for a cleaner expression of the next stage. Bitcoin can become that expression because it sits outside the crowded AI equity trade while still benefiting from the same technological regime shift.

The rise of AI agents strengthens the case, even though the commerce side of agents is taking longer than the coding side. Coding agents moved faster because the task environment is more structured, feedback loops are clearer, and the ROI is easier to measure. Commerce agents are harder. Once agents start transacting, consuming compute, managing subscriptions, purchasing services, authorizing workflows, or moving value across digital environments, the stakes become much higher. Mark Zuckerberg was recently reported to have told Meta employees at an internal town hall that AI agents were not progressing as quickly as planned. That detail matters because the next phase is about trust, permissions, reliability, and financial guardrails as much as raw model intelligence.

That delay clarifies the Bitcoin argument. Autonomous software will need a financial architecture around it: permissioning, settlement, authentication, auditability, spending limits, and neutral rails. The argument is not that every agent will settle directly on Bitcoin. The argument is that a world of abundant intelligence, autonomous software, and accelerating digital activity increases the value of neutral, scarce, globally available monetary assets. Bitcoin is a scarce asset sitting outside a system that is becoming more automated, more fiscal, more debt-heavy, and more difficult for central banks to manage.

That is why the deficits matter. Even if AI improves productivity, the U.S. still has large fiscal deficits and a worsening debt path. Interest expense is already a structural constraint. Mandatory spending remains the dominant long-term driver. AI may eventually improve healthcare costs, government efficiency, and productivity, but that is not here yet. The government remains dependent on nominal growth, financial repression, or both. In that environment, a Fed that becomes too hawkish risks colliding with fiscal reality and the labor-market disruption caused by AI.

This is the policy trap. AI capex and deficits make the inherited framework worry about inflation, while AI agents and digital employees create productivity leverage, disinflationary pressure, and a more fragile labor market beneath the surface. Warsh’s point is that central banking must become more forward-looking because the old data may not capture the shift in real time. He has emphasized the need for better data and a more contemporaneous understanding of the economy rather than relying only on government data with mismeasurement problems and surveys that may no longer be relevant.

For Bitcoin, the next rally could be about unlearning the Fed. Investors have been trained to assume that strong nominal activity automatically means a more restrictive central bank. In an AI economy, strong output can coexist with weaker labor bargaining power, lower unit labor costs, and rising potential growth. That is a new monetary-policy puzzle. The market has priced the first half of that puzzle through the AI demand impulse. It has not yet priced the second half through the AI supply impulse.

Bitcoin has been pressured by AI capital crowding and by a hawkish repricing of the Fed. Both pressures may now be close to exhaustion. Equity momentum is crashing, and AI beta is becoming harder to harvest through the same crowded infrastructure names. At the same time, the market may have gone too far in assuming the Fed will respond to AI demand without recognizing AI supply. If Warsh’s framework gains traction, investors may begin to see that AI creates more than demand for data centers. It creates productivity leverage, labor disruption, financial automation, and a need for new monetary guardrails.

Bitcoin is one of the few assets that can benefit from all of those shifts at once. The next rally may begin as a rotation trade, then evolve into something bigger. It can start because momentum hunters need a new beta trade as AI equities become too volatile. It can continue because rate expectations stop moving against it. It can accelerate if investors begin to understand that AI is forcing the Fed, the economy, and the financial system into a new paradigm.

The art of unlearning the Fed is also the art of relearning Bitcoin. The Fed-cut question is giving way to a broader question: whether Bitcoin benefits from a world where the Fed’s inherited framework is no longer enough. In an economy defined by deficits, digital labor, productivity leverage, capital concentration, and autonomous agents, Bitcoin’s role becomes clearer. Bitcoin may still trade with liquidity, but the larger argument is that the next monetary regime will be harder to manage than the last one.

AI agents have risen rapidly this year because of major model advancements, and the next step is that they are about to become consumers. That was the most important message from the keynote. Stripe was not just announcing new payments products. It was showing that crypto, stablecoins, and programmable financial guardrails are becoming core infrastructure for the AI economy. For years, crypto has been searching for a use case that felt both inevitable and practical. Stripe suggested that agentic commerce may be that use case. As AI agents begin to act on behalf of consumers and businesses, money will need to move at machine speed, across borders, in tiny increments, with clear permissions, spending limits, fraud controls, and auditability. That is where stablecoins, digital wallets, real-time settlement, and programmable payments begin to matter.

The keynote made this clear when Stripe showed agents not just writing code, but building, deploying, buying, and selling software. The demo of Stripe Projects allowed an agent to deploy an application from the command line. Then another agent used the Machine Payments Protocol and Link’s new wallet for agents to autonomously purchase a $2 API review. The point of the $2 API review was not the size of the transaction. It was that Stripe showed a practical application of the Machine Payments Protocol, where one agent could recognize that payment was required, understand how to pay, use a payment credential, receive approval, and complete a real economic exchange with another agent. That turns agentic commerce from a futuristic idea into a standardized transaction flow. The consumer was no longer clicking through a checkout page. The agent was acting as the buyer. Stripe’s message was that the internet is moving from human-centered commerce to agent-mediated commerce.

That shift requires a new financial architecture because agents cannot operate inside the old checkout world. Today’s commerce infrastructure was designed for humans: pricing pages, forms, passwords, card entry, and confirmation screens. Agents need machine-readable product data, programmable spending credentials, identity controls, fraud detection, and user-defined financial policies. One of the most important comments in the keynote came from Meta’s Ginger Baker, who said that payments will move from being a “moment” to being a “policy.” That is the right framework. In the agentic economy, consumers will not approve every single purchase manually. They will set rules: spend up to a certain amount, use this payment method, never exceed this limit, only buy from approved merchants, or require confirmation above a threshold. The human is not removed from the loop; the human moves higher in the loop. Instead of approving every click, the consumer defines the policy, the guardrails, the limits, and the trusted counterparties, while the agent handles execution. Commerce becomes less about isolated transactions and more about delegated intent.

This is where crypto and stablecoins become more important than they have been in previous cycles. The keynote repeatedly emphasized that many of the new agentic business models require payment systems that can settle instantly, globally, and in very small amounts. Stripe’s demo of Metronome and Tempo showed agents burning tokens while stablecoin payments streamed in real time. The business model was described as “tokens paid as burned.” That is a major preview of where AI-native monetization is heading. If agents are consuming inference continuously, then billing has to become continuous too. You cannot easily do that with cards, ACH, or traditional banking rails. You need programmable money that can move at the same speed as software.

The broader implication is that stablecoins are evolving from a crypto trading instrument into a settlement layer for the machine economy. Stripe’s framing was not “crypto for crypto’s sake.” It was crypto as infrastructure. Digital asset accounts, stablecoin payouts through Link, stablecoin-backed card issuing, and Tempo’s payment-focused blockchain all point toward the same conclusion: the AI economy will need money that behaves more like data. Patrick Collison reminded the audience that Stripe’s original insight was that “money is data.” In the agentic era, that idea becomes even more powerful. If agents are going to transact autonomously, then money must be programmable, permissioned, composable, and available wherever the agent is operating.

This also changes the role of the consumer. The consumer does not disappear, but the consumer increasingly becomes a principal who delegates tasks to agents. Today, a person searches for a product, compares prices, reads reviews, enters payment information, and decides whether to buy. In the next phase, the consumer may simply express intent: find me the best flight, replenish groceries, buy the right gift, negotiate a software subscription, or source a product that matches my preferences. The agent will do the work. It will search, compare, validate, transact, and potentially even return or dispute. The consumer becomes less of a clicker and more of a rule-setter. The agent becomes the operating layer between desire and transaction.

That will change the global economy because it will compress the distance between demand and fulfillment. If billions of consumers and businesses are represented by agents, then discovery, checkout, payments, fraud prevention, credit, and settlement all become faster and more automated. Stripe’s partnerships with Google, OpenAI, Microsoft, Meta, and Shopify show how quickly the pieces are coming together. Products will be discoverable inside AI surfaces. Shopify’s catalog can make billions of products legible to agents. Stripe’s Agentic Commerce Suite can handle checkout, payments, and fraud. Link can give agents controlled spending power. Radar can detect token theft, multi-account abuse, and pay-as-you-go fraud. The key point is that the agentic economy requires not just better AI models, but a complete financial control system around them.

For investors, entrepreneurs, and policymakers, Stripe Sessions should be viewed as a preview of the next phase of the internet. The first phase digitized information. The second digitized commerce. The third is beginning to digitize economic agency itself. Agents will not just help users think, write, and code. They will increasingly spend, sell, negotiate, subscribe, meter, bill, and settle. That is why Stripe Sessions mattered. It showed that the agentic commerce stack is no longer theoretical. The rails are being built now. And if consumers are increasingly being represented by agents, then the global economy is moving toward a world where money, identity, trust, and software all converge into programmable economic infrastructure.

The more time I spend helping people learn how to use AI, the more convinced I am that success with AI is less about technical ability and more about mindset. To borrow from Ralph Waldo Emerson, a line I have often used with my children about life, AI is a succession of lessons that must be lived to be understood. You do not really learn it by reading instructions. You learn it by running into roadblocks, adjusting, and trying again.

The people who are moving fastest are often the people with little knowledge of how a computer works. They are not always the youngest. They are not always the ones with the most impressive resumes. The people who seem to adapt best usually have something else: an entrepreneurial mindset.

They are willing to experiment. They are willing to get stuck. They are willing to be wrong. They are willing to treat roadblocks as information.

That last part matters.

Many people still approach AI with an assembly-line mindset. They want exact instructions. They want step one, step two, step three. They want the machine to behave predictably. They want to know that if they follow the recipe, the cake will come out exactly the same every time.

That is understandable. Most of us were trained that way. School, work, and institutions often rewarded the person who found the answer, followed the process, and completed the assignment.

AI asks for a different mindset.

AI is probabilistic. It predicts, generates, and weighs possibilities. It gives you an answer based on probabilities, context, training data, your prompt, and the patterns it believes are most likely to be useful.

This creates frustration for some people. They ask a question, get an imperfect answer, hit a roadblock, and conclude the tool failed. They ask the same question twice and get two different answers.

Others react differently. They look at the same roadblock and ask, “What did I just learn?”

Those are the people who will win.

Because AI is a probability machine. To use a probability machine well, you need a probability mindset.

For me, that mindset comes from three places: Annie Duke and thinking in bets, horse racing and emerging markets as Bayesian training grounds, and a lifelong love of Sherlock Holmes, mysteries, and the art of observation.

Put those together and you get the operating system I believe people need for the AI age.

Think in bets.

Update like a Bayesian.

Observe like Sherlock Holmes.

Thinking in Bets

I have written about Annie Duke many times because her work has had a lasting impact on how I think about decisions, uncertainty, and life. Her book Thinking in Bets is one of those books I believe everyone should read.

My connection to Annie’s ideas is personal. I have strong memories of sitting and talking to her about probabilities, poker, work, teaching, and the ups and downs of life. It reinforced a lesson that becomes more important every year: the world gives us incomplete information, hidden variables, randomness, and feedback. Most importantly, because everything is probabilistic, going back and becoming obsessed on decisions that had a different outcome than you hoped, wastes time and energy. Expect losses and move on.

That is poker. That is investing. That is parenting. That is entrepreneurship. That is life.

And now, that is AI.

The central lesson of Thinking in Bets is that we should separate decision quality from outcomes. A good decision can lead to a bad outcome. A bad decision can lead to a good outcome. The result does not always tell you whether the process was right.

That idea is critical for AI.

When someone uses AI once, gets a bad response, and says, “This is useless,” they are judging the entire process from one hand of poker. One output becomes the full verdict.

But one output is just information.

Maybe the prompt was too vague. Maybe the model needed more context. Maybe the user needed to ask for options before asking for a conclusion. Maybe they needed a different role, a better example, a clearer constraint, or a second model to judge the first model’s response. I wrote maybe here to be nice. In my experience, I always assume these things for my prompts.

The better AI user says, “That answer showed me how to improve the next prompt.”

That is thinking in bets.

Every prompt is a wager. You are betting that this question, framed this way, with this context, will move you closer to a useful result. Sometimes it does. Sometimes it does not. The skill is improving your odds with each attempt.

This is why the entrepreneurial mindset matters so much.

An entrepreneur expects the first version to be a test. The first product is a prototype. The first customer reaction is feedback. The first obstacle is data. The first failure becomes part of the project.

That is exactly how people need to use AI.

AI rewards the person who keeps playing the hand intelligently.

The Bayesian Advantage

The second part of the AI mindset is Bayesian thinking.

Bayesian thinking sounds complicated, but the practical meaning is straightforward: you update your beliefs as new information arrives.

You start with a view. New evidence comes in. You adjust. The stronger the evidence, the more you adjust. The weaker the evidence, the less you adjust.

I was trained in this long before AI.

One of my earliest forms of training in probability was handicapping horse races. Horse racing is a brutal but beautiful classroom for uncertainty. You study the form, the track, the pace, the jockey, the trainer, the distance, the weather, and the odds. You form a view. Then new information changes the picture. A horse looks different in the paddock. The odds move in the last minutes. The track changes. A pace scenario becomes more or less likely due to weather or a scratch.

You are constantly updating.

Then I began my career trading emerging markets, which may be one of the greatest Bayesian training grounds in finance. In emerging markets, each day can feel like a month. Political events, currency shocks, liquidity gaps, policy changes, capital flows, rumors, and surprises all hit at once. The world moves faster than your model.

You learn quickly that rigid thinking is dangerous.

You need a view, and you need the ability to adjust the view. You need conviction, and you need to know what would change your mind. You need confidence, and you need humility in the face of new evidence.

That is the Bayesian muscle.

AI requires the same muscle.

In the old world of work, going in the wrong direction was expensive. If you wrote the wrong report, built the wrong deck, created the wrong spreadsheet, or started the wrong project, you could lose hours, days, or weeks. Because the cost was high, people became defensive. They wanted certainty before starting. They wanted approval. They wanted the perfect plan.

AI changes that.

The cost of going in the wrong direction has collapsed.

You can draft the memo, test the argument, build the outline, generate the code, summarize the research, create three versions, and compare them quickly. If the first direction is weak, you pivot. If the second direction is better, you update. If the third version reveals something you had not considered, you follow the signal. I use five different models on the same research and narrow it down based on the new information.

That is the real unlock.

AI makes exploration cheap.

When exploration becomes cheap, the best users are the ones who update fastest during the process.

They say, “That taught me something.”

They say, “Let’s test another direction.”

They say, “This output is not right yet, but it tells me what to ask next.”

They say, “My original idea was X, but after comparing X, Y, and Z, I now think Y has better odds.”

This sounds simple, but it is not how most people were trained. Schools and companies often reward answers, completion, and confidence. AI rewards adaptive intelligence.

The person who can say, “Here is my current hypothesis, but let’s test it,” is going to outperform the person who needs every step defined before beginning.

The person who can say, “This draft is raw material,” is going to improve faster than the person who stops at the first roadblock.

The person who can say, “The evidence changed, so my view changed,” is developing the exact mindset AI demands.

That is how intelligence works under uncertainty.

That is how an AI mindset is created.

The Sherlock Holmes Problem

The third piece of the AI mindset comes from another long-running personal obsession of mine: Sherlock Holmes, mysteries, and the art of observation.

I have always loved mysteries because they are really stories about information. The facts are there, but not all facts matter equally. Some clues are signal. Some are noise. Some are distractions. Some look irrelevant until the whole case turns around them.

The detective’s job is to notice what matters.

That is also the job of the AI user.

AI gives you abundance. More answers. More summaries. More drafts. More charts. More ideas. More angles. More arguments. More possibilities.

But abundance creates a new problem: filtration.

When information was scarce, access was the advantage. In the AI world, access is becoming less scarce. The advantage moves to judgment. Can you tell which output is useful? Can you see which line contains the insight? Can you spot the assumption that does not hold? Can you identify the missing variable? Can you recognize when the model is being fluent but shallow?

This is where the Sherlock Holmes mindset matters.

Holmes solves cases by observing each situation as unique. He looks for the clue that does not fit. He pays attention to the small detail everyone else ignores. He avoids forcing every mystery into the shape of the last mystery he solved.

That is an important lesson for AI.

A lot of people want universal prompts. They want one magical formula. They want one process that works every time. But AI works best when you become more observant.

What is this specific problem asking for?

What context does the model need?

What role should it play?

What information is missing?

What would a good answer look like?

What would make this answer wrong?

What clues in the output suggest the model misunderstood?

That is the observationalist approach.

The observational user wants understanding. The observational user says, “Let me understand what is happening so I can decide what to do next.”

That difference compounds.

You are building a relationship with AI.

The Entrepreneurial Mindset Wins

This is why I keep coming back to the entrepreneurial mindset.

Entrepreneurs are used to uncertainty. They are used to incomplete information. They are used to trying things before they know if they will work. They are used to roadblocks. They are used to pivoting.

That is the AI environment.

AI is a workshop. It is a lab. It is a trading desk. It is a detective board. It is a poker table. You are constantly testing, updating, filtering, and improving.

People who want exact instructions can still benefit from AI, but the real upside comes from learning how to move through uncertainty. The moment something breaks, the entrepreneurial user studies it. The moment the model gives a strange answer, the entrepreneurial user treats it as information. The moment the path is unclear, the entrepreneurial user starts testing.

That is where the compounding begins.

The strange answer becomes a clue. The roadblock becomes feedback. The bad draft becomes raw material. The failed prompt becomes a new data point.

You do not need the machine to be perfect because your job is to work with the machine.

That is the real mindset shift.

AI rewards judgment.

It rewards the person who can think in probabilities.

It rewards the person who can update without ego.

It rewards the person who can filter signal from noise.

It rewards the person who can keep going when the first answer is not good enough.

The New AI Operating System

The AI age requires a new operating system.

Think in bets, because every prompt is a wager and every output is information.

Update like a Bayesian, because the best path will often reveal itself only after you begin.

Observe like Sherlock Holmes, because the value is in knowing which clues matter.

That is the mindset I am trying to teach.

The goal is not one perfect prompt. The goal is a better way to think.

AI will help you improve the odds.

It will help you see more possibilities. It will help you test more directions. It will help you move faster. It will help you get unstuck. It will help you learn from wrong turns. It will help you build the first version so you can react to it, improve it, and move again.

But you still have to bring the mindset.

You still have to bring curiosity.

You still have to bring judgment.

You still have to bring the willingness to be wrong and keep going.

That is why I believe the great divide in the AI age will be between people who use AI mechanically and people who use AI entrepreneurially.

One group waits for the answer.

The other group improves the odds.

One group stops at the roadblock.

The other group studies the roadblock.

One group wants certainty before beginning.

The other group begins, learns, updates, and keeps moving.

And once you understand that, AI becomes much less intimidating.

It is probabilistic leverage.

The people who thrive will be the ones who know how to use that leverage: thinking in bets, updating as the evidence changes, and observing carefully enough to find the clues everyone else missed. Most importantly, AI is a succession of lessons that must be lived to be understood.

The most important AI story right now is speed.

Every few months, the models get better. Then, suddenly, every few weeks, the models get better. Then one day you look around and realize that the thing many people dismissed as a chatbot has become a coding partner, a research assistant, a financial analyst, a tutor, a strategist, and a personal operating system.

That is what the arrival of Fable 5 represents to me.

The specific model matters less than the direction. The direction is obvious. The frontier is moving faster, the intelligence is becoming more usable, and the gap between the people using AI and the people ignoring AI is widening.

There is a very important example hiding in plain sight. Since February 28, much of the market’s attention has been pulled toward the back-and-forth in the U.S.–Iran war, the risk around the Strait of Hormuz, and the possibility of another oil shock. That focus is understandable. But while everyone was watching geopolitics, the AI model layer kept accelerating. By my count, there have been roughly two dozen notable model releases, model-family launches, previews, or major capability rollouts since then. Anthropic alone shows the speed of the cycle: Claude Opus 4.7, then Opus 4.8, then Mythos Preview, and then Fable 5/Mythos 5. OpenAI released GPT-5.4 and GPT-5.5. DeepSeek released V4 Preview with a 1-million-token context window. Google, xAI, Mistral, Moonshot, MiniMax, Qwen, and NVIDIA all pushed forward as well. The point is simple: AI is not waiting for the world to calm down.

That is the part I do not think most people understand yet.

The risk is no longer that AI disappoints. The risk is that AI keeps improving while many people continue to treat it like a novelty. They try it once, ask it a question, get a generic answer, and decide they understand it. They use it like Google with a personality. Then they move on.

Meanwhile, the models are moving from answering questions to doing work.

That is the divide.

Some people are still asking AI to write a paragraph. Others are asking AI to build tools. Some people are still debating whether AI is overhyped. Others are using it to build personal systems that make them smarter, faster, and more capable.

That second group is beginning to separate.

Last week, I saw something that made me more excited than almost anything I have seen in AI this year.

I uploaded a project for subscribers (ai.22vresearch.com)

showing how to build a Jensen Huang knowledge brain. The idea was simple: take transcripts, organize the source material, connect it to an AI workflow, use an API key, and build something that allows you to ask better questions of a focused body of knowledge.

This was not meant to be a Silicon Valley engineering project.

It was meant to be a bridge for people just like me with no prior coding experience.

The goal was to take people who had mostly used AI as a chatbot and show them that they could build something. They could gather information. They could structure it. They could connect it. They could create a tool that reflected their own curiosity. The could feel empowered.

The response I received in the days after was incredible.

People who had never coded before were building. Adults who had been intimidated by AI were sending me messages saying they had done it. Some of them were proud in the way people are proud when they realize they can do something they thought belonged to another class of person.

Then the next wave came, which was even better.

Those same adults were showing it to their kids.

That is when the whole thing clicked for me.

This was not just a knowledge brain. This was agency.

For the last year, AI has been discussed mostly through fear, market speculation, job loss, regulation, national security, and competition. All of those topics matter. I spend a lot of time on them. The macro implications are real. The investment implications are enormous. The national security implications are arriving faster than most people expected.

Yet at the personal level, the most important question is much simpler.

Do you feel more powerful because of AI, or do you feel more powerless?

That is the entire game.

The people who feel powerless will wait. They will watch the headlines. They will listen to the doomers. They will tell themselves they are too busy, too old, too nontechnical, too late, or too far removed from the center of the action.

The people who feel powerful will build small things.

That is how this starts.

A knowledge brain does not need to begin with Jensen Huang. It can begin with anything you care about. Your favorite investor. Your favorite author. Your company’s internal documents. Your health research. Your industry. Your sales calls. Your podcast library. Your notes. Your family history. Your fantasy football preparation.

That last one is where this gets fun, especially as we approach the beginning of training camps.

As fantasy football season approaches, adults and kids are already starting to talk about building their own fantasy football brains. Imagine taking your favorite podcasts, transcripts, rankings, injury discussions, coaching comments, team previews, and draft strategy shows, then turning that material into your own AI-powered research assistant.

Suddenly, AI is no longer abstract.

It is no longer a scary headline about job replacement. It is no longer a debate about whether the models are conscious. It is no longer just a stock-market argument about capex, chips, power, and margins. It is no longer a way to secure a job. It is fun.

It is a kid asking better draft questions.

It is a parent and child building something together.

It is a fantasy football brain that knows the voices you trust, the analysts you follow, the players you care about, and the strategies you want to test.

That is the on-ramp.

Most people do not become AI users because someone explains artificial general intelligence to them. They become AI users because they build one thing that matters to them. Once they build that one thing, their relationship with AI changes. The intimidation fades. The curiosity rises. The next project becomes easier.

That is exactly why I built my AI subscriber paywall around three ideas: Signal, Alpha, and Agency.

Signal means staying current.

The AI world is moving too fast for people to follow casually. The headlines are noisy. The incentives are messy. The doomers are loud. The hype men are louder. One side wants you terrified. The other side wants you euphoric. Neither is useful by itself.

Signal is about my YouTube channel filtering the noise and understanding the regime. What is actually changing? Which model release matters? Which infrastructure bottleneck matters? Which company comment matters? Which government action matters? Which part of the AI economy is accelerating, and which part is digesting the last wave of investment?

If the models are speeding up, the first job is to stay oriented.

Alpha means turning that signal into investment insight.

AI is not just a technology story. It is a capital cycle. It is an infrastructure buildout. It is an energy story, a chip story, a data center story, a networking story, a software story, a security story, and eventually an application story.

The winners will not be limited to the companies with the most famous chatbots. The buildout is much bigger than that. There will be beneficiaries across the stack, from power to chips to infrastructure to models to applications. The goal of the paywall is to take the signal and translate it into company-level opportunities, because investors need more than excitement. They need a map.

Agency is the third piece, and right now it may be the most important.

Agency means using AI yourself.

It means prompts. Tools. Workflows. Experiments. Buildouts. Examples. Mistakes. Iteration. It means watching someone else do it and realizing you can do it too.

The knowledge brain project was the clearest example yet. It gave people a path from passive user to active builder. It showed them that the API key, the transcripts, the code, and the AI assistant were pieces they could learn to assemble. Once they assembled them, they did not just have a tool. They had proof.

Proof matters.

Proof changes identity.

A person who says “I do not know how to code” becomes a person who says “I built a knowledge brain.” A parent who worries their child is falling behind becomes a parent helping that child build a fantasy football research system. A subscriber who thought AI was something happening to the world begins to see AI as something they can use to shape their own world.

That is the message I want people to hear.

You do not need to become an AI researcher. You do not need to become a professional programmer. You do not need to understand every detail of model architecture, inference scaling, token economics, or frontier benchmarking.

You need to start building.

Start with something you love. Start with something familiar. Start with a small body of information and ask how AI can make it more useful. Build a brain around a person, a topic, a hobby, a market, a sport, a company, or a question you cannot stop thinking about.

The first version can be messy. It should be messy. The goal is momentum.

The models are going to keep improving. Fable 5 will lead to the next model, and the next model will lead to another step change after that. The people who wait for the perfect moment will discover that the perfect moment keeps moving away from them.

The people who start now will compound.

They will learn how to ask better questions. They will learn how to structure information. They will learn how to connect tools. They will learn how to separate good answers from lazy ones. They will learn how to turn AI from a chatbot into a collaborator.

That is why I am so excited.

The knowledge brain response showed me that the audience is ready. Adults are ready. Kids are ready. Investors are ready. Builders are ready. The curiosity is there. The only thing missing for many people is a path.

That is what I want this platform to provide.

Signal to know what matters.

Alpha to understand who benefits.

Agency to make sure you are not just watching the AI revolution from the sidelines.

The world can always give you a reason to wait.

The models will not.

Build the brain before the gap gets wider.

To get things started, I am still tired as I write this. It has been a long couple weeks.

By now, if you have been consuming my content across Substack and YouTube, you probably know I am a Knicks fan. Not a casual fan. Not someone who discovered them when they became relevant again. I mean the kind of fan who carries the team as part of his own emotional history. If you listened to this week’s conversation with Anthony Pompliano, I went into detail about my connection. The Knicks are not just a team I like. My relationship with them as a fan is major part of my development to who I am today. Like the ups and downs in life, I remember where I was during the bad seasons, can recall the false starts, the hopeful trades, the unforgettable plays, the heartbreaks, and the years when optimism felt irrational but somehow still came back every October.

So this past week ending was not just a sports week for me. It was personal.

The Knicks won their first championship in over 50 years, and I was fortunate enough to be invited to Game 4 of the 2026 Finals against the Spurs. I have been to big games before. I have watched historic moments. I have spent a lifetime around markets, pressure, cycles, momentum, and reversals. But what happened inside Madison Square Garden that night was something different.

It was not just a game.

It was a human event.

The first thing I remember about Game 4 was not the final score. It was the sound.

Madison Square Garden did not feel like an arena that night. It felt alive. It felt like one giant nervous system made up of thousands of people who had waited their whole lives for the same thing. You could feel the anxiety before you could hear it. You could feel the history in the building. This was not just a crowd watching basketball. This was a city, a fan base, and generations of memories, many of them dominated by disappointment, all compressed into one room.

The game became historic because it was the largest comeback in NBA Finals history. But that fact alone does not explain why it will stay with everyone who was there. Records are clean. Memories are not. Records turn chaos into one sentence. But the actual experience of living through that comeback was messy, exhausting, emotional, and uncertain.

What made it unforgettable was how it happened.

It was not easy. It was not cinematic in the way sports documentaries make these things feel afterward. There was no single clean moment where everything turned and everyone knew the story had changed. It was a grind back from a beating in the first half. The Knicks had to climb back possession by possession. Every time the Garden started to believe, the Spurs pushed back. The momentum stalled. Hope appeared, disappeared, and then returned louder.

That is what made it feel so much like life. Two steps forward, one step back. Belief tested, then tested again. A little progress, then doubt. A run, then a mistake. A roar, then silence. This was a constant swing from it’s over to they have chance. The human brain wants the clean story after the fact, but the human body remembers the uncertainty in real time.

That is what everyone in that building experienced together. We were not watching a comeback. We were enduring one. And for that night, everyone felt like family.

That is why championships matter so much.

They are not simply about winning. They are about what winning does to memory for a community.

A championship takes every frustrating season for every individual and turns it into part of the community story. It takes the bad teams, the missed chances, the arguments, the jokes, the hope that seemed foolish, the years of waiting, and somehow folds all of it into one shared emotional release. Suddenly the suffering has a purpose. Suddenly the waiting becomes part of the value. Suddenly all those years you thought were wasted become the reason the moment feels so powerful.

That is the strange alchemy of sports.

The pain is not erased. It is redeemed.

For Knicks fans, this championship was not just about the players on the floor in 2026. It was about everyone who came before them. You were reminded of that by the past players in attendance at every game. It was about the versions of ourselves who watched the team when there was no rational reason to keep watching. It was about childhood. It was about parents and children. It was about friends. It was about New York. It was about the Garden.

And ultimately, it was about the shirt.

There is an old idea that fans are not really rooting for the players because the players change. They are rooting for the shirt. On the surface, that sounds cynical. But I think it is actually profound.

The shirt is not just laundry.

The jersey is a vessel.

It carries place. It carries memory. It carries identity. It carries the rituals of being a fan. It carries the games you watched with your father, the conversations you had with your friends, the seasons you complained through, the nights you said you were done and then came back again two days later. It carries a city’s personality. It carries stubbornness. It carries loyalty. It carries disappointment. And when the waiting finally pays off, it carries joy.

That is why Josh Hart’s postgame comment after the Knicks won the championship on Saturday night hit me so hard. He said, “Nobody understands the pressure of wearing that jersey.”

That line summarized the whole thing.

The pressure of wearing that jersey is not just the pressure of a basketball game. It is the pressure of memory. It is the pressure of expectation. It is the pressure of fans who have poured part of their lives into something that, on paper, makes no sense.

Why should a team matter this much?

Why should a logo create this much emotion?

Why should a shirt connect strangers?

Because it is not really about the shirt. It is about what the shirt holds.

That is the part of sports that artificial intelligence cannot replace.

AI is going to create infinite entertainment. It will generate highlights that never happened. It will recreate historic games with different endings. It will make personalized videos, synthetic announcers, deepfake interviews, simulated athletes, and customized storylines. The internet already made media abundant. AI will make it overwhelming. We are moving into a world where the question will not be, “Can content be created?” The answer will almost always be yes.

The question will be: Was it real? Did it happen? Were you there? Did you feel it with other people?

That distinction is going to become one of the most important cultural and economic questions of the AI age.

When anything can be generated, authenticity becomes scarce.

When any image can be faked, proof becomes valuable.

When any performance can be simulated, presence becomes premium.

When entertainment becomes infinite, the real human experience becomes the luxury good.

That is what I felt at Game 4. I was not there because the visual quality was better than television. In some ways, watching at home gives you a better view. You get replays, commentary, angles, statistics, and comfort. But being there gives you something technology cannot replicate. It gives you shared emotion in real time. It gives you the feeling of strangers becoming a community for three hours. It gives you the physical memory of sound moving through your body. It gives you the knowledge that you were inside the moment before history knew what it was going to become. This is why for those who could not attend the game, they went to watch parties.

And the way I happened to be there made the connection even deeper and also symbolic.

I was invited by people associated with Candy Digital, a company built around the intersection of sports, collectibles, digital ownership, and fan identity. That context mattered. I do not want to make this piece about Candy Digital specifically, but sitting there with people who have been thinking deeply about the future of sports collectibles made the entire night feel like a real-world case study. This was not an abstract debate about NFTs. This was the emotional source code of why they may matter.

Because if NFTs have a future in sports, it will not be because people want another speculative image. It will be because people want authenticated memory. They will want proof that they were part of something real. They will want digital objects that connect them to physical experiences, communities, teams, places, and moments that cannot be recreated after the fact.

This playoff run made that even clearer because the story was not confined to Madison Square Garden. Knicks fans were everywhere. After Game 4 on my subway ride home, they packed the subways not from the game but from the bars and watch parties. They traveled. They showed up in opposing arenas. They turned road games into something that felt, at times, like extensions of New York. You could feel it in the chants, in the noise, in the way the orange and blue kept appearing in places where it was not supposed to dominate. Back home, the city started to feel like one giant fan base. Knicks hats, shirts, and jerseys became hard to find because everyone wanted a visible marker of belonging. That was the real story underneath the basketball. People were not just buying merchandise. They were putting on identity. They were saying, “I am part of this.” In that sense, the community was not a side effect of the championship run. It was the center of it.

This is where I think NFTs were misunderstood when they were first acknowledged by the world.

The first wave of NFTs was too focused on speculation and images. People thought of them as digital art, profile pictures, or trading vehicles. Some of that mattered. Much of it did not. But the deeper idea behind NFTs was never only about JPEGs. The deeper idea was about digital ownership, provenance, identity, community, and proof.

Those words matter more now than they did before AI.

Community. Memory. Emotion. Identity. Authenticity. Provenance. Belonging. Proof-of-presence.

That is where sports and NFTs eventually meet.

A Game 4 NFT should not be thought of as a speculative collectible. It should be thought of as a digital memory object. It is the modern version of a ticket stub, except richer, programmable, and connected to a community. It says: I was there. I was in the building when the Garden shook after OG Anunoby’s tip went in. I was part of that comeback. I did not just watch the highlight later. I lived the uncertainty. I felt the fear. I felt the release. I belonged to that moment.

But the more important point is that the NFT does not have to stop with attendance. It can become a community credential. It can say you were part of the run, part of the road takeover, part of the citywide identity shift, part of the emotional network that made the championship feel larger than the team itself. A hat or jersey tells the world what you care about in the physical world. An NFT can do something similar in the digital world, but with memory and proof attached. It can carry the story of where you were, what you experienced, and which community you belonged to when the moment happened.

That has value.

Not because someone can flip it the next day. Not because it has a floor price. Not because it belongs in a hype cycle. It has value because human beings have always collected proof of meaningful experience. We save ticket stubs. We frame jerseys. We keep programs. We take photos. We tell stories. We pass memories down. NFTs are simply the digital evolution of that instinct.

In sports, the best NFTs will not be about replacing the physical world. They will be about connecting the physical and digital worlds. They can authenticate attendance. They can unlock communities. They can connect fans who shared the same moment. They can become badges of loyalty. They can attach to video, stats, memorabilia, access, fantasy games, or future experiences. They can turn a fan’s personal history into a verified digital identity.

That matters because the future of fandom will not only be about consuming content. It will be about belonging to something.

In an AI world, live sports may become even more important, not less. Sports are one of the few remaining forms of mass culture where the outcome is unknown, the emotion is live, and the community is real. A championship game is not generated content. It is a collective human experience. It cannot be recreated afterward in a way that equals the original, because the uncertainty is part of the experience. The fear is part of it. The doubt is part of it. The waiting is part of it.

The more AI floods the world with synthetic media, the more valuable it will be to prove that something was real. The more entertainment becomes personalized and artificial, the more people will crave shared experiences that cannot be individually generated. The more deepfakes blur the line between truth and fiction, the more important authenticated memory becomes.

That is why the Knicks championship meant so much to me. It was not just the end of a drought. It was a reminder of what technology cannot manufacture.

AI can create a perfect image of a Knicks celebration. It can write a fake recap. It can generate a video of a crowd roaring. It can simulate the sound of Madison Square Garden. But it cannot give you the feeling of standing there as belief slowly returned to the building. It cannot reproduce the exact emotional weight of waiting decades for something and then realizing, in real time, that it might actually happen. It cannot manufacture the life you lived before the moment arrived.

That is the human premium.

The future may be increasingly digital, but the most valuable digital assets will be the ones that point back to something deeply human. Not synthetic status. Not artificial scarcity. Real memory. Real presence. Real community. Real emotion.

Game 4 reminded me that the most powerful experiences are not consumed alone. They are shared. They move through a crowd. They attach themselves to place. They become part of who we are.

That is why I believe NFTs still have a future in sports. Not as hype. Not as speculation. Not as technology looking for a use case. But as authenticated containers for the things fans already care about most: memory, identity, community, emotion, and proof that we were part of something bigger than ourselves.

The Knicks won a championship. I was there for one of the games that helped define it. And years from now, the box score will still tell people what happened.

But it will never tell them what it felt like.

That is why the experience matters.

That is why the memory matters.

And that is why, in an age of artificial intelligence, the most valuable thing may not be what can be generated.

It may be what can be proven to have been lived and I have lived with the Knicks!

That may seem strange at first, but the reason is simple. Change is hard for humans because our biological brains are built to seek comfort, safety, pattern recognition, and stability. Exponential change is even harder because our linear brains are not naturally wired to process a world that is moving faster, compounding faster, and forcing us to adapt faster than at any point in our lives. For that challenge, philosophy may be more useful than technology.

This is also why I have been thinking more about how to expand my own work. On my YouTube channel, I have spent the last two years trying to help investors understand macro, markets, AI, and the speed of technological change in a broader framework. Soon, I plan to add a dedicated channel focused on the crypto ecosystem, but not in the narrow way crypto is often discussed. The goal will be to look at crypto the way traditional investors look at the macro world: through liquidity, flows, infrastructure, adoption, regulation, incentives, network effects, and the changing architecture of the financial system. My belief is that a major shift is coming driven by the parabolic agentic rise and asset bridge of tokenization, and to see it clearly, investors need to stop treating crypto as a collection of isolated tokens and start analyzing it as an emerging financial ecosystem being pulled forward by AI, agents, stablecoins, tokenization, and programmable settlement.

The Daily Stoic, published in 2016, is a modern, day-by-day guide to Stoic philosophy, using short passages from thinkers like Marcus Aurelius, Seneca, and Epictetus to help readers build discipline, perspective, patience, and emotional control. Its lasting power comes from the reminder that humans have been wrestling with the same internal anxiety, fear, ambition, frustration, and chaos for thousands of years. That was true even in eras when life expectancy was far shorter and the external problems of war, disease, hunger, political instability, and survival were far greater than most people can imagine today. The tools change. The headlines change. The assets change. But the inner experience of uncertainty does not change nearly as much as we think.

In the book, there is a simple line that captures one of the hardest parts of investing: “Games and seasons are constituted by seconds.” The point is not that the long term does not matter. It is that the long term is not experienced as a clean, elegant chart moving from the lower left to the upper right. It is experienced one second, one day, one headline, one drawdown, and one emotional test at a time.

That is especially true in crypto today. The long-term story has arguably never been clearer. Artificial intelligence is accelerating. The agentic revolution is moving from theory to workflow. Stablecoins, tokenization, and digital financial rails are becoming necessary infrastructure for a world where software agents, humans, institutions, and machines need to transact globally and continuously. And yet, despite that clarity, the daily experience often feels like a depressing game of whack-a-mole. Every time the thesis feels obvious, price action fails to confirm it. Every promising development is met with another rotation, another liquidation, another regulatory headline, another failed breakout, or another reminder that markets do not move on our preferred timeline.

That emotional disconnect is the real challenge. As investors, we want the future to announce itself in the price today. We want certainty in the long-term thesis to produce certainty in the short-term chart. But markets rarely work that way. The AI buildout is visible because it is physical: chips, data centers, power, cooling, networking, memory, and electricity. The crypto buildout is less visible because much of it is financial plumbing: settlement rails, tokenized assets, stablecoin liquidity, custody, compliance, wallet infrastructure, identity, and guardrails for agentic activity.

The fact that this infrastructure is harder to see does not make it less important. If anything, it may make it more important. A world of AI agents cannot scale on yesterday’s financial system alone. Agents need money. They need permissions. They need settlement. They need trusted rails. They need financial guardrails that allow autonomous or semi-autonomous economic activity without chaos. That is a bullish foundation for crypto, even when daily prices refuse to reward the patience required to see it.

The other important distinction is that the physical AI buildout will inevitably face bottlenecks because it is constrained by atoms. Chips, power, cooling, transformers, networking equipment, memory, land, permits, and skilled labor all depend on real-world supply. Even if demand is obvious, the supply response cannot instantly appear. Crypto is different. Once agent-driven financial activity begins to accelerate, the growth can behave much more like software than infrastructure. Stablecoins, tokenized assets, wallets, smart contracts, and settlement networks can scale through code, liquidity, and adoption rather than waiting years for factories, transmission lines, or power plants. That does not mean there will be no constraints around regulation, security, or trust, but it does mean the upside can express itself through network effects in a way that looks far more like the software platform bull market than a traditional industrial cycle.

That is why the eventual shift in investor attention could be so powerful. The market has already shown this year how quickly investors can respond when token usage, stablecoin volumes, or network activity begins to validate a thesis. Once investors start to see AI agents not only as users of compute but as future users of money, settlement, identity, and financial permissions, the narrative can change quickly. In the physical AI buildout, demand can run into supply. In crypto, demand can compound through usage. When that happens, the same investors currently frustrated by the lack of price confirmation may suddenly realize that the financial guardrail layer has the potential to produce the type of parabolic network-effect moves they once associated with software platforms, and earlier generations associated with crypto itself.

At the same time, price can never be ignored and right now, it is saying investors are not focused on the upside with crypto. The market is a real-time summary of the emotions, beliefs, positioning, liquidity needs, and time horizons of every participant. It is not always right about the future, but it is always telling you something about the present. That distinction matters. A market that refuses to go up despite good news may be telling you that belief has not yet converted into urgency. A market that sells off on bullish developments may be telling you that positioning, leverage, or fatigue is still more powerful than the narrative. A market that starts rising and holds gains may be telling you that skepticism is being absorbed and demand is finally overwhelming supply.

The goal is not to fight the market simply because you believe you are right. The goal is to respect the long-term thesis while waiting patiently for the market to confirm that the trend is ready. Patience is not passivity. Patience is preparation. It is the discipline to do the work before the price action makes it obvious, and then have the confidence to get more excited when the market itself begins to agree.

This is where the Stoic framing matters and why The Daily Stoic has been a part of my daily reading since 2017. The Stoic does not deny discomfort. The Stoic does not pretend volatility is pleasant or that waiting is easy. The Stoic simply refuses to let the immediate problem erase the larger reality.

In markets, the immediate problem is always loud. A token is down. A stock is lagging. A breakout failed. A competitor rallied more. A narrative went quiet. Someone on social media declared the trade dead. All of this is happening while parabolas seem to be appearing everywhere except the place where many investors were trained to expect them first: crypto. These daily irritations create anxiety because they force investors to relitigate the thesis over and over again.

But the right question is not whether every day feels good. The right question is whether the underlying world is moving toward or away from the thesis. On that measure, the answer still points in one direction: more AI, more agents, more digital activity, more demand for programmable financial infrastructure, and more need for crypto-native rails. The mistake is allowing the daily price action to become more real than the structural shift. The equal mistake is ignoring the price action altogether. Reality requires holding both truths at once.

This does not mean every coin wins, every valuation is justified, or every dip should be bought without discipline. Stoicism is not blind optimism. It is realism. The bullish case for crypto must be paired with standards: real usage, credible networks, liquidity, security, regulation, institutional access, and integration into the emerging AI economy. It must also be paired with trend awareness.

The best investors do not need to buy every bottom or predict every turn. They need to understand the direction of the world, watch how the market is digesting that direction, and then wait for the moment when narrative, fundamentals, liquidity, and price begin to align. That is when patience starts to pay. That is when the market tells you it is time to get excited. Until then, the job is to observe without panic, prepare without forcing, and keep separating temporary frustration from permanent impairment.

At some point, the spotlight of momentum will begin to shift. For the seven months, the market has been obsessed with the agentic driven physical buildout of AI: semiconductors, data centers, power, cooling, networking, memory, and all the infrastructure required to turn intelligence into a scaled industrial system. That spotlight has been deserved. But the next phase of the AI story will not only be about building the machines. It will also be about building the financial guardrails that allow those machines, agents, businesses, and humans to interact safely, legally, and efficiently. That is where crypto, stablecoins, tokenization, and programmable settlement become harder to ignore.

The key is not to force that transition before the market is ready. As Marcus Aurelius reminds us in a passage often associated with Stoic discipline, “What stands in the way becomes the way.” For crypto investors, the thing standing in the way right now is the tape itself. Good news has not consistently been rewarded. Bad news has not yet been easily absorbed. Momentum has not fully confirmed that the market is ready to shift its attention from the physical AI buildout to the financial infrastructure layer. But that obstacle is also the signal. When good news starts to work, when bad news can be handled, when breakouts hold, and when the market begins rewarding the financial guardrail theme instead of dismissing it, that will be the tape telling us the season has changed. I think that day is coming soon. But until the market confirms it, the right posture is patience, preparation, and respect for the process.

So the discipline is to return to the process. Games and seasons are constituted by seconds. Markets and technological revolutions are constituted by days that often feel confusing, frustrating, and unrewarding. The AI buildout, the agentic revolution, and the construction of financial guardrails are the season. The daily candles are the seconds. If investors let every second determine their belief in the season, they will almost certainly lose focus at the moment when focus matters most.

The task is not to ignore price, but to put price in its proper place. Price is feedback, not final truth. Volatility is information, not destiny. Anxiety is a signal, not a strategy. The reality is that the world is moving toward more compute, more automation, more digital settlement, and more need for crypto-enabled financial infrastructure. The Stoic investor’s job is to hold that reality in view, brick by brick, while patiently waiting for the market to confirm when belief has become trend.

On my weekly AI-Macro-Crypto YouTube show, the podcast I have referenced the most about the impact of AI on our lives and investments has been Moonshots with Peter Diamandis. Last year, I remember driving in Maine, looking out over the ocean on a beautiful sunny summer day, and listening to an episode titled Tech Experts Break Down the Incoming AI-Crypto Collision That Will Redefine Global Power. At the time, AI was still considered a bubble in the minds of most institutional investors, and crypto was still largely misunderstood. But as I listened, I knew I would eventually come back to that conversation when investors were forced to accept that AI and crypto were not separate stories. They were two parts of the same coming financial and technological transition.

That belief was built around the one thing I thought would change investors’ doubts about AI: agents. AI agents speed up adoption because they move AI from a tool that answers questions to a system that takes actions. They also help answer the “where are the revenues?” question by turning intelligence into workflows, automation, software development, trading tools, business processes, and eventually economic activity. 2026 has brought the rise of AI agents, both as a driver of market alpha and as a source of pressure on software and other long-duration assets. Agents increase demand for tokens, compute, and real-time coordination, but they also create uncertainty around the terminal value of traditional SaaS. Investors are currently focused on semiconductors, optical fiber, data centers, and hardware, but many of those are cyclical areas of the AI buildout. Unlike SaaS, which grew steadily and tied to payrolls along with nominal GDP, the capex buildout of AI will be subject to cycles around shortages, bottlenecks, input inflation, and margin compression. As wealth managers, pensions, and long-term investors again look for technology growth that is not disrupted by AI and does not require ever-rising capex intensity, the crypto guardrails become much more important. The AI-crypto collision is no longer theoretical. It is here now.

As one guest on the Moonshots podcast put it when discussing the GENIUS Act last year, it may be “the most significant economic legislation and changes that we’ve seen in our lifetimes.” He went even further, calling it “as big a shift in our economy as I think we’ve ever seen.” What made the moment so important was not simply crypto itself, but the creation of legal guardrails around stablecoins, tokenization, and digital assets. In other words, the United States is beginning to build the new financial rails for an AI-driven, internet-native economy, one where, as the podcast said, “When we give our AI agents access to that, we’re going to see an explosion in the economy.”

This discussion was not about meme coins, speculation, or another trading cycle. It was about the economic guardrails of the global system beginning to change and go digital to serve digital agents. One of the most important lines in the conversation was that we are moving into an age where you cannot rely on the Swift network, three-day settlement, and high transaction costs in a world being accelerated by AI. The podcast’s deeper point was that AI and crypto are not separate stories. AI increases the need for faster coordination, faster settlement, faster capital allocation, and programmable systems. Crypto provides the rails. Stablecoins become programmable money. Tokenization becomes programmable ownership. AI agents become the future users of both. That was the theory. The recent DoorDash stablecoin news and the acceleration of tokenization are the evidence that this theory is now moving from podcast conversation to market structure.

That is why this paper is the follow-up to my last piece on programmable money. In that piece, I wrote about why the DoorDash stablecoin news mattered more than it first appeared. The point was not simply that another company was exploring faster payouts. The point was that money itself was beginning to behave differently. In the DoorDash example, stablecoins showed how money can be distributed, routed, and managed at the moment it is created. A customer pays, and that payment can be split instantly between the platform, the driver, and the merchant. No batching. No unnecessary delay. No separate reconciliation process after the fact. That was the first step in the evolution of the crypto financial guardrails: money becoming programmable. Tokenization is the next step because it applies the same logic to ownership. Assets, shares, funds, collateral, private investments, and eventually entire portfolios can begin to move with the same software-driven logic. Stablecoins change how money moves. Tokenization changes how ownership moves. When those two forces combine, the financial system starts to become programmable.

The important part is that this progress is happening while many investors are still waiting for “clarity” from Washington and while crypto sentiment remains subdued. The conversation remains focused on the slow movement of the CLARITY Act and the still-uncertain regulatory path for crypto market structure. But the infrastructure is not waiting. Stablecoins now have more than $272 billion in global circulating supply and $10.2 trillion in adjusted transaction volume over the last 12 months, according to Visa’s on-chain analytics. Nasdaq received SEC approval to allow certain securities to trade and settle in tokenized form, initially focused on Russell 1000 companies and ETFs tied to major benchmarks like the S&P 500 and Nasdaq 100. Bullish announced a $4.2 billion acquisition of Equiniti, a major transfer agent serving more than 20 million shareholders and processing roughly $500 billion in annual payments. Securitize partnered with Computershare, which services more than 25,000 companies and 58% of the S&P 500, to help U.S. companies issue tokenized shares while preserving dividends and proxy voting. These are not isolated headlines. They are the plumbing phase of a new financial network, and it is happening now.

The venture capital market is starting to confirm the same message. Andreessen Horowitz’s crypto arm recently raised $2.2 billion for its fifth dedicated crypto fund, even though the industry is still recovering from the excesses of the last cycle. The important part is not just the size of the fund. It is the focus. The firm highlighted stablecoins, tokenization, perpetual futures, prediction markets, and AI agents as some of crypto’s most promising areas for investment. That list matters because it is not built around the old Web3 hype cycle. It is built around financial infrastructure, market structure, and software-driven coordination. Stablecoins are becoming the money layer. Tokenization is becoming the ownership layer. Prediction markets and perpetual futures are becoming new venues for price discovery. AI agents may become the future users of these programmable rails. The Moonshots podcast warned that the combination of AI and crypto could accelerate the economy in ways most people still do not understand. The capital now being raised around these same themes suggests serious investors are beginning to position for that possibility.

This is why tokenization matters so much. Tokenization is the process of representing ownership of an asset as a digital token on a blockchain or distributed ledger. That asset can be a Treasury bill, a money market fund, an ETF, a share of stock, a private company interest, a real estate claim, a private credit instrument, or eventually almost anything that can be legally represented, verified, transferred, and settled. At first glance, that may sound like a technology upgrade. In reality, it is a market-structure upgrade. The financial system looks instantaneous from the outside, but inside the machine it is still full of settlement cycles, custodians, transfer agents, clearinghouses, fund administrators, reconciliation systems, market hours, batch processing, and legal recordkeeping. Tokenization attacks those delays. It turns ownership into something that can be programmed, transferred, settled, collateralized, and integrated with software. The Moonshots discussion used a simple real estate example: if ownership can be verified instantly and represented digitally, then dormant value can become collateral faster, ownership can become fractional, and assets can become usable in ways the old system made difficult. That is the shift. It is not just faster trading. It is a new relationship between ownership, liquidity, and collateral.

But the deeper story has always been larger than faster settlement or fractional ownership. The real story is the eventual merging of two financial universes: roughly $800 trillion of traditional financial assets and roughly $3 trillion of crypto assets that have been living on separate rails. Tokenization is not just the bridge between those worlds. It is the Trojan horse. It allows traditional assets to move onto crypto rails without forcing investors to think they are leaving the regulated financial system behind.

This is also where tokenized ETFs may become one of the most important bridges. The ETF was already one of the great financial innovations of the last 30 years because it turned a basket of securities into a liquid, tradable product. Tokenization can take that idea further. A traditional ETF is a basket. A tokenized ETF can become a programmable portfolio container. That container could eventually hold public equities, tokenized Treasuries, stablecoins, crypto tokens, private credit, private company interests, real estate, infrastructure assets, and other real-world assets. In the old system, these lived in separate markets with separate custodians, separate settlement systems, separate access points, and separate investor bases. In the tokenized system, they can increasingly become components of the same programmable portfolio. This is how crypto tokens, public companies, and private companies begin to merge. Crypto moves into regulated investment wrappers. Public companies can exist as traditional shares and tokenized shares with the same legal rights. Private company ownership can gradually become more standardized, fractionalized, transferable, and usable inside broader investment products. The public market becomes more programmable. The private market becomes more accessible. The ETF becomes the bridge between the two.

That is the wake-up call for investors. The mistake is to think of crypto only in terms of bull markets, bear markets, halving cycles, liquidity cycles, and token prices. The more important story is the infrastructure buildout happening underneath the surface. Stablecoins are becoming programmable cash. Tokenized Treasuries can become programmable collateral. Tokenized ETFs can become programmable portfolios. AI agents will become the active users of these rails because they cannot operate inside a legacy financial system built around batching, settlement delays, and manual reconciliation. We have seen this adoption curve before. Think back to the years just before the App Store and the rollout of mobile broadband. The plumbing had to be laid before the platform and social media eras could explode. We are in that same infrastructure phase today. But this time, the rails are connecting a new economy where the consumer is both human and digital. AI agents will be the catalyst that ignites the network effects across this system. The same dynamic we are already seeing in AI token usage could eventually show up in crypto transaction volumes. As agents move from answering questions to taking actions, they will create more transactions, more settlement events, more collateral movements, more portfolio rebalances, and more automated payments. In other words, the velocity of money can rise because software agents do not operate on human time. They operate continuously. If agent usage is already producing parabolic-looking charts in tokens consumed, compute demand, and semiconductor-related revenues, then programmable money and tokenized assets could produce similar parabolic pressure on financial volumes as agents begin to transact. This is not only an upside story for digital assets. It is a severe margin-compression threat to any financial business model that depends on friction, float, restricted access, or unnecessary delay.

That is why the AI-crypto collision matters even more now than it did when I first listened to that Moonshots episode. This year has become the year of AI agents. The conversation has moved from chatbots answering questions to agents taking actions, writing code, managing workflows, searching across systems, and beginning to operate as digital workers. Once agents start interacting with money, portfolios, collateral, payments, and ownership, the need for programmable financial rails becomes much more urgent. AI agents cannot fully operate in a financial system built for banking hours, settlement windows, and manual reconciliation. They need programmable money and programmable ownership. Even JPMorgan, led by Jamie Dimon, one of the most outspoken critics of Bitcoin and crypto over the years, is now showing how seriously it takes tokenization. The firm has argued that tokenization will help reshape ETFs and the broader funds industry, and it is already running tokenized ETF proof-of-concepts through Kinexys. That is the signal. The train is leaving the station. The Moonshots conversation called this a Pandora’s box of innovation. That is the right framing. The box is opening while sentiment is still subdued. Investors waiting for perfect clarity may miss the fact that the new financial guardrails are already being built. This is not a world where crypto replaces everything. It is a world where crypto rails are absorbed into everything. That is the next network effect. And it is already beginning.

The Moment I Saw the Shift

Last week I attended an event for public pension funds. I was there to talk about AI and crypto. Having given a similar presentation to endowments and foundations in late January, what struck me most was not simply how much the AI narrative had changed in three months. It was how much my own life with AI had changed in just three months. I decided to write this paper and release it for 22V (https://22vresearch.com/) but the requests have come in to release it more broadly as investors try to understand the upward movement in stocks the last two months while the oil doomers fight the GDP and EPS power of AI .

On the JetBlue flight to the event, I spent nearly the entire five hours working on my laptop, going back and forth between large language models and my AI assistant, OpenClaw, back in Brooklyn. At the endowment and foundation event earlier this year, OpenClaw was just gaining global attention. Software stocks and other industries were under attack from fears of obsolescence driven by Claude. It was on that trip that, because of OpenClaw, I ordered the first of many Apple hardware purchases since then. By the time I stood in front of the pension audience last week, the shift was obvious. AI progress is a locomotive, and three months of change is almost impossible to comprehend. AI was no longer just the operating system of how I work. I now had digital employees working for me all day.

That reflective experience felt like a microcosm of what the entire world is going through right now. Standing on stage in front of CIOs and allocators responsible for decisions that influence tens of trillions of dollars, I could see the gap between the speed of change and the speed of institutional response. Watching the survey results and listening to panel discussions, I did not feel that investors fully grasped the structural shift that has already occurred. Even I, someone immersed in this every day, only recognized the magnitude of the last three months after stepping back and reflecting on it.

The world has shifted faster than portfolios and most investors can adjust. AI adoption itself is taking longer than the expansion of AI capabilities, and many institutional investors are not set up to make dramatic shifts quickly. That is the key point. Benchmarks are still weighted for the world that won the last decade, not necessarily the world that will win the next one. This will be a decade of benchmark arbitrage because investors will adjust more slowly than AI and crypto are moving. In my view, 2026 will be remembered as the beginning of the rise of AI agents, and with that rise, the investment opportunity has moved from the software world into the physical world.

The End of the Margin Era

For the last fifteen years, the dominant investment phrase was Jeff Bezos’s famous line: “Your margin is my opportunity.” It was the perfect description of the software era. Code scaled globally. Distribution costs collapsed. Network effects created winner take all markets. The largest technology companies used software, platforms, data, and cloud infrastructure to attack profit pools across media, retail, advertising, enterprise software, transportation, and finance.

The result was a historic period of margin capture and market concentration. A small number of companies became the dominant drivers of equity returns, index performance, and investor imagination. They were the winners of the software age, and because they won so decisively, they now dominate the S&P 500, the MSCI World, and the way most investors think about growth.

That era is not over because software no longer matters. Software still matters enormously. But the opportunity has shifted. The next decade will not be defined only by who writes the best code. It will be defined by who can build, power, cool, connect, manufacture, and deploy the physical infrastructure required for intelligence to enter everything. The threat to the code moat built by humans is AI’s ability to convert ideas into monetization in minutes.

The new phrase is no longer “your margin is my opportunity.”

The new phrase is “your CapEx is my opportunity.”

This is one of the most important investment changes of our lifetime. Artificial intelligence has moved from the digital world into the physical world. It is no longer only a story about models, applications, and software productivity. It is now a story about the heavy infrastructure layer required to scale intelligence: chips, power, cooling, chemicals, optical networks, data centers, advanced packaging, memory, batteries, automation, robotics, and the reindustrialization of the global economy.

The world spent the last decade optimizing for asset light software businesses. The next decade will require an enormous asset heavy buildout.

The Five Layer AI Economy

Jensen Huang has described the coming transition as a roughly $90 trillion physical world upgrade. Whether one focuses on that exact number or the broader direction, the message is clear: AI is not just a data center CapEx cycle. It is not just hyperscalers buying GPUs. It is a full stack rebuild of the global economy so intelligence can be embedded into every company, every factory, every device, every car, every phone, every robot, and eventually every workflow.

The data center is only the beginning. The larger opportunity is the conversion of the physical world into an AI native operating system.

That is why the five layer AI cake is such a useful framework. At the top are applications and workflows. Below that are models and AI platforms. Beneath that is data infrastructure and management. Then come chips, compute, storage, and networking. At the base are energy, hardware, manufacturing, and commodities.

Investors naturally gravitate toward the top of the stack because the top looks like the old world. It looks like software. It looks like margins. It looks like scalability. At the top, there is now abundance. But the constraint is increasingly at the bottom, where scarcity and bottlenecks lie. AI demand is no longer limited by imagination. It is limited by the physical stack: power availability, heat, land, permitting, substations, memory, networking, and materials.

That means the most important investment question is changing. In the software era, the question was: which company can take someone else’s margin? In the AI infrastructure era, the question is: who receives the CapEx dollars required to make intelligence ubiquitous?

This is where benchmark arbitrage begins.

Why the Benchmarks Are Wrong

The major global equity benchmarks still reflect the winners of the last era. The S&P 500 and MSCI World are heavily weighted toward the companies that dominated the software, internet, platform, and cloud age. That made sense. Those companies generated enormous returns, expanded margins, and built deep competitive moats. But benchmarks are backward looking by design. They tell you who won the last cycle, not necessarily who will receive the marginal dollar in the next one.

There is also a momentum element inside the construction of these indexes. The biggest weights become the biggest weights because they were the winners. Their market capitalizations rise, the indexes allocate more capital to them, passive flows reinforce that dominance, and the process continues. In the case of the Mag 7, that dominance took most of the 2010s to build. It was a long compounding process. The world gradually moved toward software, cloud, mobile, digital advertising, e commerce, and platforms, and the benchmarks slowly came to reflect that reality.

As someone who grew up being trained to handicap horse races, I always go back to what Charlie Munger said:

“The model I like, to sort of simplify the notion of what goes on in a market for common stocks, is the pari mutuel system at the racetrack. If you stop to think about it, a pari mutuel system is a market. Everybody goes there and bets, and the odds change based on what is bet. That is what happens in the stock market.”

The current market weightings reflect the bets people have made about who they think the winners of the future will be. Historically, when change was more linear, you had time to adapt your views. AI is different because AI is moving like a locomotive. The speed and power of this transition are already creating visible strain across the physical economy. Data center demand is running into the limits of the grid, the construction cycle, the permitting process, the semiconductor supply chain, and the materials needed to build it all. The bottlenecks are not theoretical. They are the evidence that the physical world is being forced to respond to a digital intelligence wave moving far faster than the capital stock was built to handle.

That is why I use the phrase benchmark arbitrage, even though this is not benchmark arbitrage in the traditional sense. Most arbitrage situations are thought of as short term events. An index addition. An index deletion. A rebalance. A forced buyer. A forced seller. A gap that closes over days, weeks, or months.

This is different. This is structural. It may take years for the benchmarks to fully reflect the new AI economy. But the size and speed of the change make it feel like an event happening right now. The arbitrage is not that an index committee is about to make one adjustment. The arbitrage is that the real economy is already changing faster than the benchmark can evolve.

If the next decade is defined by AI CapEx, then today’s benchmarks are likely underweight the areas that matter most. They are underweight the physical inputs required to scale intelligence. This includes power, electrical infrastructure, advanced manufacturing, chemicals, optical connectivity, semiconductor equipment, packaging, and the fragmented industrial supply chains now sitting directly in the path of the AI spending wave.

This creates a rare moment. Investors can look at the world not as it is currently represented in the benchmarks, but as it may need to be represented ten years from now. That is benchmark arbitrage. It is a structural mismatch between where capital is currently allocated and where the physical economy must go.

The Cost of Underinvestment

The irony is that the prior software era helped create this opportunity. For years, capital flowed toward asset light businesses and away from asset heavy industries. Investors rewarded recurring revenue, high gross margins, buybacks, low capital intensity, and terminal value stories built on long duration cash flows. At the same time, many parts of the physical economy were neglected. Commodity capacity was underbuilt. Grid infrastructure aged. Industrial supply chains became optimized for cost, not resilience. Manufacturing was pushed offshore. Hardware became less fashionable than software.

Now AI is exposing the cost of that underinvestment.

The same investors who spent years rewarding companies for needing little capital now have to recognize that AI requires enormous capital. The winners are not only the companies deploying AI. They are also the companies selling the inputs needed for everyone else to deploy AI.

If every Fortune 500 company needs its own AI infrastructure, if every country wants sovereign AI, if every factory needs automation, if every car becomes an AI computer on wheels, if humanoids move from concept to production, and if every device becomes intelligent, then the bottlenecks will define the profits.

The receivers of the CapEx dollars become the new margin takers.

The Terminal Value Problem

This also explains why software has become more difficult to value. AI is disrupting the terminal value philosophy that supported many long duration assets. In the old model, investors could look three, five, or ten years out and assume that dominant software franchises would continue compounding with limited disruption. But AI changes the speed of competition. Coding is becoming cheaper. Intelligence is becoming more widely available. The cost of building software is collapsing.

That does not mean every software company fails. It means the durability of future margins is harder to underwrite. When the pace of change becomes exponential, the confidence interval around terminal value widens. A business that looked unassailable three years ago can suddenly face new competition from AI native workflows, agents, open source models, cheaper code generation, or a customer deciding to build internally rather than buy another software seat.

The market is beginning to understand that software may still be valuable, but the old assumptions about duration, pricing power, and defensibility need to be reexamined.

This is the other side of benchmark arbitrage. The old winners are not disappearing, but their dominance was built for a slower world. Their index weights reflect years of compounding in an era when software had scarcity value, code created durable moats, and terminal values could be modeled with more confidence. AI compresses that timeline. It questions the durability of some software margins at the same time it creates urgent demand for the physical inputs required to scale intelligence.

That is what makes the current moment so unusual. The market is not waiting ten years to recognize the strain. It is seeing it now across the physical supply chain. The indexes still carry the weight of the last era, but the bottlenecks are already pointing to the next one.

The Speed of Code Meets the Speed of Steel

Hardware and commodities face the opposite dynamic. They were ignored because they were messy, cyclical, capital intensive, and fragmented. But those are precisely the characteristics that can create opportunity when demand shocks arrive. If supply is slow to respond and demand accelerates, pricing power can emerge in unexpected places.

The world cannot prompt its way into more electricity. It cannot instantly create more transformers. It cannot magically permit new data centers, manufacture more high bandwidth memory, expand advanced packaging capacity, or produce the specialty chemicals required for leading edge semiconductors overnight.

The digital world moves at the speed of code. The physical world moves at the speed of steel, copper, silicon, chemistry, energy, and regulation.

That gap is the investment opportunity.

AI is forcing the fastest moving technology in history to collide with some of the slowest moving supply chains in the economy. That collision creates bottlenecks. Bottlenecks create pricing power. Pricing power creates earnings revisions. Earnings revisions eventually force benchmark weight changes. The investor’s job is to identify those changes before the benchmark does.

Follow the Bottlenecks

The old world was about concentration. The new world is about fragmentation. The Mag 7 captured the margin in the software age because software rewards scale, distribution, and network effects. The AI physical buildout rewards a much broader ecosystem. The winners may include semiconductor companies, packaging suppliers, memory producers, power equipment manufacturers, grid operators, industrial automation companies, thermal management firms, optical networking providers, specialty chemical companies, construction firms, and commodity producers.

The opportunity spreads across countries, sectors, and supply chains.

That does not mean every hardware or commodity company is a winner. It does not mean investors should blindly buy anything related to AI infrastructure. The point is more precise. The world is moving from a software dominated investment regime to a full stack AI buildout regime. In that world, the most attractive opportunities may appear in places that benchmarks still treat as secondary.

Investors need to stop viewing AI only through the lens of applications and start viewing it through the lens of constraints. Where is the shortage? Where is the bottleneck? Where is the underinvestment? Where does permitting take years? Where does supply require physical capacity? Where are the receivers of the CapEx dollars?

That is the new map.

This is also why I am currently building my thematic portfolio (https://ai.22vresearch.com/) around the five-layer AI cake. If the opportunity is moving from the software layer into the physical infrastructure layer, then active management has to evolve with it. The goal is not simply to own a static basket of AI winners. The goal is to understand where we are in the cycle, where the bottlenecks are forming, where capital is flowing, and how the weights should shift across the stack as the 90 trillion dollar buildout progresses. Early in the cycle, that may favor semiconductors, advanced packaging, power equipment, and optical connectivity. As the cycle broadens and commodity shortages and power constraints take move, the focus may move toward data centers, energy, chemicals, cooling, automation, and eventually applications, agents, and humanoids. Benchmark arbitrage only becomes actionable if it can be translated into portfolio construction, active reweighting, and a disciplined process for moving capital along the cake as the AI economy evolves.

The software era taught investors to follow margin capture. The AI era will teach investors to follow capital expenditure. The previous winners made money by using code to compress costs and attack incumbents. The next winners may make money because everyone else must spend capital to survive.

The world has changed. The opportunity has shifted. The benchmarks still reflect the last era, but the physical world is already being rebuilt for the next one.

Your margin was my opportunity.

Your CapEx is my opportunity.

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

We are watching the financial guardrails of the global system begin to change in real time, but the shift is subtle enough that it still looks like a series of isolated decisions. That is how systems evolve. Not through a single break, but through convergence. Over the last 18 months, four very different companies, Stripe, Uber Technologies Inc., DoorDash, and Morgan Stanley, have each, in their own way, positioned for the same future. Infrastructure, operators, platforms, and banks are all aligning around a single idea: the system that governs how money moves is being rewritten, toward a world where money is global, instantaneous, and programmable.

This is not a payments upgrade. It is a change in how money behaves.

Stripe had already learned what did not work in years past: volatile crypto as a consumer payment method. By acquiring Bridge in October 2024, it was effectively saying what does work: stablecoins as programmable, global settlement infrastructure. At the time, many viewed the move through a narrow lens, primarily as a play on remittances or cross-border payments. But that framing missed the bigger point. This was not about improving one category of payments. It was about rethinking the underlying rails those payments run on. Bridge was focused on solving the fragmentation of global money movement, abstracting away foreign exchange, banking intermediaries, and settlement delays. Stripe wasn’t chasing a narrative. It was addressing a structural inefficiency: the fact that money still moves as if borders define it, even though information no longer does.

That insight surfaced more explicitly at a Bloomberg Tech event in June 2025, when Uber Technologies Inc. CEO Dara Khosrowshahi described stablecoins as a practical tool for global companies. His framing was notable not for what it included, but for what it left out. There was no discussion of speculation, decentralization, or ideology. The focus was purely operational: stablecoins could reduce the cost and friction of moving money globally. That was the signal. Adoption would not be driven by belief, it would be driven by efficiency.

Then, this week we got the confirmatory move, DoorDash began exploring stablecoin-based payouts across more than 40 countries. At first glance, this looks like the natural continuation of the trend: another company looking to improve payment efficiency. But that interpretation misses what is actually changing.

DoorDash is not just improving how money moves. It is changing how money works.

At its core, DoorDash operates a marketplace. A customer pays for a service, and that payment must then be split between the platform, the driver, and the merchant. In today’s system, that process is delayed and fragmented. Funds are collected, routed through multiple intermediaries, and distributed later through internal reconciliation systems. Time is required. Intermediaries are required. Reconciliation is required. These are not just inefficiencies, they are structural features of the financial system.

Stablecoins remove those features and replace them with something fundamentally different.

They allow money to be distributed at the exact moment it is created.

In a programmable system, the logic of the transaction is embedded directly into the payment itself. When a customer completes a transaction, the platform can take its share instantly, while the driver and merchant receive their portions in real time. There is no batching. No delay. No reconciliation process that happens later. The distribution of value is not a separate step, it is part of the transaction.

This is the shift.

In the legacy system based on friction and middlemen, money is collected first and distributed later. In a programmable community-based system, money is distributed at the moment of creation.

That difference collapses an entire layer of financial infrastructure.

It removes the need for settlement windows. It reduces reliance on intermediaries. In doing so, it signals the death of the ‘float’, the multi-billion dollar friction point where banks have historically captured value while money sits in transit.“ It simplifies operations that have historically required complex back-office systems to manage. And most importantly, it changes the role that time plays in the financial system. Time is no longer a requirement for coordination, it becomes optional.

This is why the DoorDash moment matters more than the steps that came before it.

Stripe identifies the inefficiency.
Uber recognizes the use case.
DoorDash executes the model.

And that model is not just faster payments. It is programmable money.

The implications extend far beyond delivery platforms.

Any system that relies on collecting and later distributing money, ridesharing, e-commerce, global payroll, and supply chains, can be restructured around this model. The need for delayed settlement, internal reconciliation, and fragmented payout systems begins to disappear. What replaces it is a continuous flow of value, where transactions and distributions happen simultaneously.

This is where the idea of financial guardrails begins to change.

For decades, the system has relied on friction to enforce order. Delays allow time for reconciliation. Intermediaries provide oversight and control. Geographic boundaries determine how money flows and where it can go. These constraints are part of how the system maintains stability.

Programmable money replaces those constraints with code.

Instead of time delays, rules are executed instantly. Instead of intermediaries, logic is embedded directly into transactions. Instead of borders, money moves on a global network. The guardrails do not disappear, they shift from institutions to software.

And that shift is already happening.

The institutional confirmation is now arriving. Morgan Stanley recently introduced a Stablecoin Reserves Portfolio designed to hold the underlying assets backing stablecoins in a compliant, liquid structure. It has also advised certain clients to allocate a small portion of portfolios to cryptocurrency and launched its own Bitcoin ETF. The message is clear: traditional finance is not standing outside the digital asset transition. It is adapting to it.

The next step is already beginning to emerge. Once money becomes programmable and moves in real time, it no longer needs to stop once it is distributed, it can be managed continuously. AI agents can begin to handle these flows automatically, allocating, saving, or investing funds the moment they are received. A driver could be paid instantly and have a portion of those earnings immediately routed into a yield-bearing account, a short-term Treasury fund, or another financial instrument based on predefined preferences. In this model, money is not just moving faster, it is being actively managed at the moment it is created. The line between payments and asset management begins to blur.

For decades, time was built into the financial system. Settlement delays, batching, and reconciliation all required it. In this new model, time is no longer a constraint, it becomes optional. Money does not wait to be processed, distributed, or even invested. It moves, allocates, and compounds instantly.

What began as an infrastructure move in 2024 has now reached execution in the real economy, with institutional support forming around it. The system is not being disrupted from the outside. It is being rebuilt from within.

And that is how structural change happens.

Not through a single event, but through a sequence.

October 2024: infrastructure adapts.
June 2025: operators recognize.
2026: execution begins.
Now: institutions align.

Each step is rational. Each step is incremental. But together, they point in the same direction.

Money is becoming global.
Money is becoming instantaneous.
And now, money is becoming programmable.

DoorDash is not just adopting a new payment method. It is demonstrating a new financial model, one where money does not wait to be distributed, but executes immediately according to predefined rules.

That is a different kind of system.

And like all structural shifts, it does not announce itself as a revolution.

It looks like a small decision.

Until it isn’t.

Something important is happening right now, and I believe it marks the beginning of Bitcoin’s next phase. For years, one of the biggest objections from investors has been simple: Bitcoin trades like a risk asset. It moves with technology. It rises and falls with software, liquidity, and speculative growth. As long as that was true, many investors felt comfortable staying in the fiat system and owning large-cap tech instead. They did not need Bitcoin because the best-performing assets were still built on code, still tied to traditional markets, and still housed inside the legacy financial system.

That relationship is starting to change.

For much of the past three years, Bitcoin was correlated to software and had to compete with short-term yields above inflation. In that environment, investors could earn a real return in cash while also owning the dominant growth stocks in technology. Bitcoin had to fight for attention. Even when it rallied, many still viewed it as a side story rather than a central macro asset. Today, that setup is beginning to break apart. Bitcoin has started to separate from its software correlation just as software itself is coming under growing pressure from exponential AI. That matters because it opens the door for Bitcoin to become something very different in investors’ minds: the only major growth asset built on code that actually benefits from AI rather than being threatened by it.

That shift is central to the whole thesis.

Software is facing a genuine disruption cycle. Mythos changed the conversation because it forced investors to think about what happens when models are no longer incremental improvements, but capability jumps. The challenge is no longer limited to software margins or enterprise budgets. It now reaches into labor, knowledge work, cyber risk, pricing power, and the durability of business models that were built for an earlier era of code. AI agents are accelerating this process because they are not just generating answers. They are searching, planning, negotiating, coding, and increasingly acting. That changes what software can charge for, what labor is worth, and what parts of the old technology stack deserve premium multiples.

Bitcoin stands apart from that pressure because it is not a software company. It does not rely on seat growth, pricing power, margins, or enterprise spending. It is digital scarcity. In a market that is reassessing everything built on code, Bitcoin may be the one code-based asset that emerges stronger as AI advances. That is a profound change, because for years investors treated Bitcoin like an extension of tech beta. What if it is now becoming the opposite: a digital asset that benefits as exponential AI weakens the rest of the code economy?

That is the first reason I believe we are entering Bitcoin’s regime.

The second reason is macro. The next phase of rising inflation appears to be arriving at the same time the jobs market is losing momentum. That combination creates exactly the kind of policy tension that can trap central banks. When inflation moves higher while labor conditions soften, the Fed has less room to fight inflation aggressively. It has to pause, hesitate, and eventually lean toward easing into an environment where purchasing power is still deteriorating. That is the backdrop in which Bitcoin historically begins to stand apart.

The key signal is simple. Year-over-year CPI is about to cross above 3-month bill yields. In other words, real short-term rates are on the verge of turning negative again. Historically, that line has been one of the most important markers for Bitcoin’s performance. When cash yields are above inflation, investors can sit in short-duration instruments and preserve purchasing power. Bitcoin has to compete with a real return. When inflation rises above those short-term yields, the equation changes quickly. Cash stops functioning as a store of value in real terms. The safest-looking nominal asset begins losing ground against inflation. That is when capital starts searching for an alternative.

Based on the framework I have used repeatedly, the strongest regime for Bitcoin is when CPI year over year is above 3-month bills and the Fed is on hold or easing. That is the quadrant where annualized Bitcoin returns have run above 200%. We are now getting very close to entering that exact window. This is not a small macro detail. It may be the most important setup Bitcoin can have.

What makes this moment especially important is that inflation is returning for a different reason than in the last cycle. This is not simply a reopening burst or a temporary commodity squeeze. Inflation is rising because the physical needs of the economy are becoming more unstable just as AI is increasing demand for real-world inputs. Energy, power, cooling, semiconductors, memory, grid equipment, industrial metals, transport, packaging, and logistics are all becoming more central to growth. AI was supposed to be a software story. Instead, it is becoming a scarcity story. The more intelligence gets pushed into the economy, the more physical capacity the economy needs to support it. That means inflation pressure is being reinforced by the same technologies that investors once thought would create only abundance.

Creative destruction has been building toward this point for decades. Every wave of innovation has made the system more efficient, more deflationary, and more unequal by rewarding capital over labor. That is a big part of how we ended up with today’s global wealth distribution problem. Each time deflation and job losses threatened the system, central bankers responded the same way: cut rates, add liquidity, launch QE, support demand, and try to offset the deflationary impact of technological change. That worked well enough in prior cycles. It will not work the same way against AI. This wave is far more powerful. Affordability is already stretched, and now policymakers are facing a collision they cannot easily solve. AI is pushing deflation into labor, software, and knowledge work just as it is pulling inflation into the physical economy through power, commodities, cooling, chips, and infrastructure. In other words, the same force that weakens wages and jobs is also raising the cost structure of the world it needs to grow. Central banks can print money, but they cannot print copper, electricity, fertilizer, or stable purchasing power. They can support markets, but they cannot stop exponential intelligence from disrupting labor. That is why this policy fight is different. Technological deflation and scarcity inflation are now arriving together, and the old QE playbook is not built for that world. Also remember, the next act brings humanoids into our lives so this is just the beginning.

There is another layer to this thesis that has become more important with Mythos: the vulnerability of the fiat system itself. For years, critics focused on quantum computing as the future threat to Bitcoin. The pressure today is coming from a far more immediate direction. Mythos has shifted attention toward hacking risk and cyber vulnerability inside the existing financial architecture. The Treasury called an urgent meeting with bank leaders, and Project Glasswing was assembled by Anthropic in response to the risks for companies. Those are meaningful actions. They show that exponential AI is forcing institutions to confront how exposed the financial system may be.

If banking systems, payment rails, software stacks, and core digital infrastructure become more vulnerable in an era of rapidly advancing models, then the fiat system starts to look less secure than investors assumed. That is a major shift in perception. Bitcoin was built for a world where trust in centralized systems erodes. The more AI exposes fragility inside those systems, the more relevant Bitcoin becomes. The conversation around safety starts moving away from abstract future risks and toward present-day institutional vulnerability.

This is why I have repeatedly said that Bitcoin is the purest AI trade. That may sound unusual because Bitcoin is often discussed separately from semiconductors, data centers, robotics, or cloud infrastructure. The deeper connection is macro. AI is reorganizing the economy around scarcity, instability, and exponential change. It is forcing massive physical investment, increasing commodity intensity, raising power demand, and transforming the software layer at the same time. Bitcoin sits at the intersection of those forces. It benefits from negative real rates. It benefits from distrust in fiat systems. It benefits from scarcity becoming more valuable than abundance. It benefits from a world in which nominal safety and real purchasing power begin to diverge more sharply.

There is also an important stablecoin driven network-effect dimension that investors may be underestimating. Bitcoin already benefits from one of the strongest networks in finance. It has the largest and accepted store of value brand in the digital economy, the deepest liquidity, the broadest global recognition, the strongest institutional acceptance, and the most secure decentralized monetary network. AI agents will strengthen that advantage even further.

As agents begin transacting across platforms, settling value, allocating capital, purchasing services, and interacting with one another at machine speed, they will favor the asset with the deepest liquidity, the clearest monetary rules, the highest uptime, and the broadest acceptance. Networks compound through trust, scale, and interoperability. Bitcoin is uniquely positioned on all three. In a world where agents are participating in economic activity alongside humans, Bitcoin’s network could become even more powerful because it offers a neutral, global, liquid rail that both people and machines can recognize. Most importantly, AI agents don’t have the bias of the comfort of the old fiat system. They will make decisions based on the best decision which will ultimately benefit Bitcoin the most.

All of this matters because the rise of AI agents may create an entirely new layer of economic activity. We are moving toward a world in which software does not just recommend or analyze. It acts. Agents will search, negotiate, purchase, settle, and optimize on behalf of users and businesses. In that kind of economy, a digital asset with fixed supply, global portability, deep liquidity, and growing institutional rails becomes more valuable. Every new participant strengthens the network. Every new wallet, institution, treasury, payment integration, custody platform, and settlement layer makes Bitcoin more useful to the next participant. If AI agents become a major source of future transactions, Bitcoin’s network effect may become one of its most powerful long-term advantages.

The credit side adds another layer to the case. As the AI disruption to software accelerates, the impact moves from public SaaS multiples into private equity marks, private credit books, software-backed loans, and broader financial conditions. Investors understand this intuitively. As confidence in growth built on code fades, capital rotates toward scarcity. That is exactly what this market has been showing. The risks of AI disruption are pushing investors toward power, metals, semiconductors, infrastructure, and increasingly toward Bitcoin. There is only one growth asset that can live in the digital world and still benefit from AI’s transformation of the code economy. That is Bitcoin.

Then there is the global dimension. The Iran war raises the probability of higher food and energy prices around the world, especially in emerging markets that depend on stable input costs. Developed markets can absorb some of that through policy flexibility, reserve currency status, and deeper capital markets. The weakest of emerging markets often feel the pressure much faster. When food, diesel, fertilizer, and imported energy costs rise, currencies come under stress and purchasing power erodes quickly. In that kind of environment, Bitcoin starts to serve a different role. It becomes more than a speculative asset or portfolio diversifier. It becomes a monetary escape valve. It becomes a place where capital can move when local currency weakness accelerates and when households or investors want an asset that cannot be diluted by domestic policy decisions. If this next inflation wave spreads globally, Bitcoin’s relevance expands with it.

The technical backdrop is now starting to align with the macro. Bitcoin’s weekly MACD has just crossed. Technical signals matter most when they line up with a major regime shift beneath the surface. That is what makes this moment so important. The technical picture is improving just as real rates are on the verge of turning negative, just as Bitcoin is breaking its correlation with software, just as AI is exposing the vulnerability of fiat-linked systems, and just as inflationary pressure is rising from the physical needs of the new economy. That is a rare alignment. Add in the incredibly lost sentiment in crypto and Bitcoin despite all of this and it suggests that Bitcoin may be doing more than bouncing. It may be entering its regime.

This is the setup investors have been missing. They keep looking for Bitcoin to behave like the old version of a risk asset. The next move may come because Bitcoin is no longer being priced that way. It is becoming the only code-based growth asset that benefits from AI rather than being disrupted by it. It is becoming the asset that fits a world of rising inflation, softening labor, scarcity, hacking risk, negative real rates, and fragile confidence in the legacy financial architecture.

That is the real point. Bitcoin does not need a new story. It needs the world to enter the conditions it was built for.

That may be happening now.

When the DOGE experiment launched last year, it was framed as the ultimate solution to government bloat. Instead, the initiative quickly dissolved, leaving disputed savings and an untouched fiscal deficit in its wake. Now, a year later, those four letters have returned to define our current reality. Only this time, DOGE stands for Debt, Oil, Growth, and Employment. This four-part framework represents a structural trap for the Federal Reserve, and navigating this exact dilemma is why, when combined with the rise of AI agents, Bitcoin is likely to become the defining story coming out of this new crisis.

The irony is hard to miss. Washington tried to sell DOGE as a story about efficiency, but markets are staring at something much larger and much harder to fix. Oil prices have surged as the war with Iran has disrupted flows through the Strait of Hormuz. After investors held onto hopes that this would quickly fade, it has now become clear that this is a much larger issue regardless of when the Strait is reopened. Inflation is set to rise in the coming months because of the breadth of the disruption to global energy. At the same time, import-price pressures were already showing up even before oil’s latest surge, while AI-driven demand has pushed memory-chip prices sharply higher, straining the supply chain for PCs, smartphones, autos, and other electronics.

That is what makes this moment dangerous. The inflation problem may be returning, but it is returning for reasons the Fed cannot easily solve, all while affordability remains a major political issue. Rate hikes do not reopen Hormuz. They do not create more DRAM. They do not suddenly lower the cost of semiconductors, memory, or other hardware inputs rippling through autos and computers. These are supply-side and geopolitical shocks landing on top of an economy that is already losing momentum.

This is where the real D.O.G.E. framework matters.

Debt is the structural constraint.
Oil is the inflation shock.
Growth will slow from inflation and a worsening credit cycle.
Employment is already weak enough that the Fed may soon have to favor that side of its dual mandate.

Start with debt, because debt is what makes this cycle different from the oil driven inflation of the 1970s. In 1970, gross federal debt was about 35.5% of GDP. By 1979, it was about 31.6%. Today, the comparable FRED series stands around 122.5% of GDP. Even before the Global Financial Crisis, the ratio was far lower than it is now. This means the United States is entering a possible second inflation wave with a debt burden roughly four times what it carried at the end of the 1970s. That one fact alone changes how much pain the system can absorb.

That matters because investors love using the 1970s as the analogy. The comparison works at a headline level: oil shock, inflation pressure, and a central bank being tested again after it thought it had made progress. But the balance sheet underneath the country is radically different now. In the 1970s, the Fed could fight inflation inside a much less indebted fiscal structure. Today, every additional point of rate pressure hits an economy, a Treasury market, and a federal budget that are all far more sensitive to borrowing costs. In other words, this is not just a replay of the 1970s. It is the 1970s problem inside a far more levered system.

The same constraint shows up in asset prices. The Fed is not dealing with inflation inside a cheap, under-owned financial system like the one it faced in the 1970s. The stock market-capitalization-to-GDP measure stands at over 200%. In the late 1970s, that figure was far lower, roughly 42% in 1975 and about 38% in 1979. The US economy has become financialized. That matters because a Fed determined to crush inflation with higher rates today would not just be tightening into a weaker labor market and a heavily indebted fiscal system; it would also be tightening into an asset market whose size relative to the economy is vastly larger than it was in the 1970s. The higher stock-market-cap-to-GDP ratio rises, the harder it becomes for the Fed to tolerate the kind of asset deflation that a true inflation fight would likely require.

The labor market is the second major difference. In 2022, when the Fed was crushing post-COVID inflation, it was doing so in an economy with strong job creation and much hotter wage growth. That gave policymakers room to prioritize inflation. The labor backdrop today is not the same. The February 2026 employment report showed nonfarm payrolls down 92,000, unemployment at 4.4%, and payroll employment having changed little on net in 2025. The unemployment rate bottomed at 3.4% in 2023. Outside of non-cyclical areas like healthcare, the labor picture looks even softer. That is not a booming labor market. It is a softening one. Wages have been in a steady decline since the peak in 2023 falling from 6.4% to 4%. This is not the kind of wage spiral that would justify engineering major labor-market damage just to counter an oil shock.

Jerome Powell has already all but described this trap. In his March 18 press conference, he said the Fed remains focused on both sides of its mandate, noted that job gains have remained low, and acknowledged that higher energy prices will likely push up inflation in the near term. He also repeated the standard central-bank instinct that policymakers often try to “look through” energy shocks, provided inflation expectations stay anchored. That language matters. It tells you the Fed is already preparing the market for the idea that not all inflation is equal, and not all inflation should be met with the same policy response.

Other Fed officials are framing the same dilemma. Vice Chair Philip Jefferson said sustained higher energy prices could worsen both inflation and spending, complicating the Fed’s dual mandate. Reuters has described the Fed as cornered between weak jobs and higher inflation. And all of this now sits in front of a leadership transition: Jerome Powell’s term as chair ends on May 15, 2026, Kevin Warsh has been nominated to replace him, and President Trump continues to publicly argue that rates should be lower immediately. That only sharpens the dilemma. A new chair may soon inherit a weakening labor market, rising inflation pressure, and open political pressure for easier money all at once.

So what happens next?

The Fed is unlikely to fight this inflation wave the way it fought the last one. That does not mean it will welcome inflation. It means it will try to distinguish between inflation caused by excess domestic demand and inflation caused by oil, war, tariffs, and hardware bottlenecks. If unemployment rises and hiring remains weak, the Fed will be pulled toward the employment side of its mandate. It may speak hawkishly to preserve credibility, but the underlying logic points toward a willingness to look through at least part of the inflation surge if the economy weakens enough. Debt makes that bias stronger. The more levered the country becomes, the less tolerance there is for prolonged real restraint.

When a central bank can no longer afford the pain of true economic discipline because the debt burden is simply too high, the market will instinctively seek an asset whose supply cannot be expanded to fund the next rescue.

And that is where Bitcoin comes in.

Satoshi Nakamoto released the Bitcoin white paper on October 31, 2008, just weeks after the financial system nearly collapsed. It was not an accident that Bitcoin entered the world in the middle of bailouts, emergency rescues, and a crisis of trust in financial institutions. Bitcoin was born as a response to a system in which governments and central banks could always create more money, extend more guarantees, and socialize more losses when the structure became too fragile to endure discipline.

That point became even clearer in the symbolism around Bitcoin’s launch. When the network’s genesis block was mined on January 3, 2009, it included a newspaper headline referencing a second bank bailout in Britain. Whether you view that as protest, timestamp, or both, the message was unmistakable: Bitcoin was created in the shadow of a monetary order that had become dependent on intervention and rescue.

Now fast-forward to today. The United States is not just dealing with an inflation scare. It is dealing with a credit-cycle problem layered on top of it. Growth is more fragile. Job creation has stalled. The fiscal position is vastly weaker than it was in the 1970s. And the inflation impulse is coming from places the Fed cannot directly repair. That is exactly the kind of setup that exposes the limits of discretionary fiat management. The central bank can talk tough, but if it must choose between defending employment and crushing supply-driven inflation inside a 122%-debt-to-GDP economy, markets should assume the threshold for easing is lower than in past cycles.

Bitcoin does not need hyperinflation for this thesis to matter. It only needs a world in which the market increasingly believes that every inflation fight will be shorter, every easing cycle will come sooner, and every debt-heavy downturn will force policymakers back toward accommodation. Ultimately, Bitcoin is the final receipt for a century spent trying to outlaw the Great Depression and suppress the Schumpeterian deflation of innovation. We traded creative destruction for a hyper-financialized trap where stocks cannot be allowed to fall, debt suffocates monetary policy, and exponential tech growth guts labor from the inside with the rise of AI agents about to change the labor force forever. That is why Bitcoin was created. Not because inflation is always imminent, but because the structure of modern government finance makes hard money harder to sustain through pain.

Crucially, this macroeconomic trap is arriving exactly as the alternative infrastructure matures. The financial guardrails are now fully built, and Wall Street ETFs have made access frictionless for everyday investors. While traditional markets face a growing liquidity crisis, highlighted by redemption gates currently slamming shut on private credit funds, the digital alternative is accelerating. Surging stablecoin volumes are rewiring global settlement, and tokenization is arriving to fundamentally upgrade the legacy rails. Add in a rapidly expanding digital economy where AI agents will increasingly execute autonomous financial decisions, and the contrast is stark. Bitcoin was engineered because we needed a better system, and for the first time, the plumbing for that system is fully operational.

The administration’s original DOGE failed because it tried to address the symptom theatrically while leaving the disease untouched. The real D.O.G.E. problem is much bigger: Debt, Oil, Growth, Employment. That is the Fed’s next trap. But this time the trap is arriving in a system with too much debt to absorb real restraint, too much asset inflation to tolerate a true purge, too little labor-market strength to justify another all-out inflation war, and too much political pressure to pretend the Fed operates in a vacuum. That is why Bitcoin matters here. It was designed for the moment when the market finally understands that the state can no longer fight every inflation shock with credibility, consistency, and pain tolerance. In a D.O.G.E. world, Bitcoin stops looking like a speculative side story and starts looking like a monetary necessity.

For the last several years, most people have understood artificial intelligence through the lens of the chatbot era: better answers for humans. That framing is already outdated. We are now moving into something much larger and far more disruptive: the rise of autonomous agents that do not simply respond to people, but act on their behalf, transact with other agents, and coordinate across both the digital and physical world. OpenClaw matters because it signals that this transition is no longer theoretical. It marks the opening of the agentic layer, where AI stops being a tool for conversation and starts becoming infrastructure for action.

From Billions of Humans to Trillions of Agents

That shift may create one of the biggest changes in economic demand architecture in modern history: a move from billions of human consumers to trillions of agent consumers. For centuries, technology changed production, labor, and distribution, but the end buyer remained human. Industrialization displaced workers, but humans still bought the goods. The internet eliminated storefronts, but humans still clicked to purchase. In the next phase, that assumption breaks. Increasingly, the direct buyer, scheduler, negotiator, and executor will not be a human. It will be an agent.

Human consumers are limited by biology, attention, time, biases, emotions and friction. They sleep. They hesitate. They compare a few options and make imperfect decisions. An agent can compare thousands of variables instantly, adjust dynamically, and keep optimizing until the transaction is complete. The idea of trillions of agent consumers is not futuristic exaggeration. It is the natural consequence of embedding intelligence into software, devices, platforms, vehicles, robots, and eventually humanoids. One person may control dozens of agents. One company may deploy millions. A smart factory will function as a dense network of agents ordering parts, purchasing power, allocating compute, managing robotic workflows, and settling transactions across suppliers and logistics networks. The number of economic actors expands dramatically even as the number of humans stays flat.

Labor: Disruption Reaches Both Sides

This has profound implications for labor. Historically, disruption replaced certain jobs on the supply side while humans remained central on the demand side. But the agentic economy is different because disruption now reaches both sides of the equation. Humans are not only pressured as workers; they are increasingly bypassed as transactional participants. More of the economy begins to consist of agent-to-agent exchanges that do not require human intervention.

That does not mean humans disappear. The labor market shifts toward supervision, orchestration, exception handling, trust design, and high-level judgment. But many old assumptions around job creation feeding back into a human-centered demand system become less reliable. In this cycle, a growing share of demand may come from non-human actors operating on machine logic rather than household psychology.

Time Compression and Velocity

Much of modern economic analysis is built around human time. Growth, productivity, and GDP are measured through frameworks shaped by work hours, pay cycles, settlement delays, and household consumption behavior. An agentic economy compresses time. It increases the speed at which work is performed, decisions are made, and transactions occur. Time has always been one of the hidden constraints on growth. Agents weaken that constraint.

That is where velocity of money enters the picture. By automating the negotiation and settlement of trillions of micro-transactions, agents drive a major increase in the speed at which money moves through the system. Nominal activity rises not only because more things are happening, but because more things are happening faster. Time compression begins to look like growth acceleration.

Fiat Friction and the Case for Programmable Money

But that acceleration runs into a problem. An agentic layer capable of near-infinite velocity cannot be cleanly plugged into financial infrastructure built around ACH, SWIFT, office hours, reconciliation delays, and human review. The faster the agent economy grows, the more obvious this friction becomes. Legacy financial rails were built for a world where humans were the primary actors. They were not built for trillions of autonomous systems settling value continuously across borders and platforms.

A world of trillions of agent consumers cannot run on trust systems designed for slow human oversight. Without programmable guardrails, the risks become enormous: runaway spending, recursive feedback loops, automated fraud, and flash-system instability at a scale legacy institutions are unprepared to manage. The future needs money and asset systems native to a world where non-human actors transact autonomously.

This is where crypto moves from speculative sideshow to strategic infrastructure. Stablecoins enable real-time settlement. Smart contracts allow conditional execution. Wallets become the operating accounts of agents. Onchain systems make ownership, permissions, and collateral legible to software. Machine commerce does not only need speed. It needs programmable constraint: rules embedded directly into the transaction layer itself. In a machine economy, compliance, authorization, risk limits, and settlement logic cannot sit outside the system as slow human overlays. They must become part of the rails.

Bitcoin, Tokenization, and the Expanding Digital Economy

Bitcoin’s role in this future is distinct from programmable money. It is the store of value layer. As I have said, it has one thing, software investments in the fiat world do not, a moat as the chosen store of value in the digital economy. As the digital economy expands through trillions of agent-driven transactions, the ecosystem of digital assets grows with it. Bitcoin benefits not because it processes machine commerce, but because it anchors the value system of an increasingly digital world. The larger the digital economy becomes, the more critical a scarce, rules-based, globally recognized digital reserve asset becomes. Bitcoin’s value proposition strengthens as the economy it sits within expands.

Tokenization extends this further. Enormous pools of wealth today sit in relatively dormant form: real estate, private equity, infrastructure, private credit. That capital cannot remain static if trillions of agents are transacting in real time and constantly requiring liquid collateral. Tokenization converts these assets into granular digital units that can be recognized, partitioned, pledged, and mobilized, transforming static wealth into active collateral usable inside the financial architecture of the machine economy.

Humanoids make the story larger still. Once agents are embodied, they become direct participants in physical commerce: ordering parts, purchasing electricity, contracting logistics, renting storage. The machine economy extends from cloud infrastructure into the real world. The consumer at the edge of disruption is no longer just a person holding a phone. It may be a machine holding a wallet.

The Real Significance

For investors, this is the real significance of this moment. The AI story is not just about smarter models or lower labor costs. It is about the emergence of a new class of economic actors. OpenClaw matters because it signals that the agentic layer is arriving now, not someday. Once that layer is in place, the number of active economic participants expands from billions to trillions. The economy speeds up, velocity rises, labor markets adjust, and legacy financial infrastructure begins to look obsolete.

That is why programmable money and digital assets stand to benefit, not as speculative enthusiasm, but as foundational infrastructure for machine-native commerce. The next great economic transition may not be defined only by smarter software. It may be defined by the moment when the consumer itself stopped being human.

The next great Bitcoin rally may begin in the least Bitcoin-looking place imaginable: private credit.

Not because private credit breaking is instantly bullish for BTC. It is not. In a real liquidity event, Bitcoin usually gets hit first along with everything else liquid. The first phase is not liberation. It is liquidation. But the second phase is where the real thesis lives. In a system this indebted, this financialized, and this politically incapable of tolerating a prolonged credit unwind, the tide of liquidity is almost never allowed to stay out for long. And when the state puts liquidity back in, Bitcoin tends to understand the meaning of that move faster than almost any other asset.

Warren Buffett gave the cleanest language for this long ago. “You only learn who has been swimming naked when the tide goes out.” He also mocked private equity’s “cherished fee structures and love of leverage,” and later warned that, at rare moments, “credit vanishes and debt becomes financially fatal.” Buffett was not talking about Bitcoin. He was diagnosing a system built on leverage, opacity, and confidence. That diagnosis applies perfectly to private credit. When the tide goes out, hidden fragilities stop being theoretical. They become the whole story.

That is why private credit matters so much right now. By Morgan Stanley’s estimate, the market was about $3 trillion at the start of 2025 and could approach $5 trillion by 2029. And the first warning flares are already visible. This week, Morgan Stanley restricted redemptions at one private-credit fund after investors sought to withdraw nearly 11% of outstanding shares, while JPMorgan marked down some loans made to private-credit funds amid growing scrutiny of software exposure. The important point is not that the entire market is already in crisis. The point is that the pressure is no longer hypothetical. It is showing up in redemptions, marks, and lender behavior now.

AI Is the Accelerant

The core vulnerability is not just leverage. It is leverage tied to an industry whose economics are being repriced in real time.

Morgan Stanley said in March that about 25% of BDC portfolios are in software. That is an extraordinary concentration when you consider what AI is doing to software economics. For years, software was financed as if recurring revenue meant durable cash flow, sticky customers, high margins, and stable exits. AI is now challenging each part of that assumption. Pricing power gets squeezed. Products become features faster. Competitive moats narrow. New compute and product investment becomes mandatory. In other words, a lot of private credit was underwritten against a version of software that may already be disappearing.

Bitcoin Is Caught in the Same Crosswinds

All of the discussion around software valuations and private credit ultimately circles back to Bitcoin. When you look at this chart showing the overlay of Bitcoin with software stocks and private equity stocks, the relationship becomes clear. Bitcoin trades like a hybrid of software beta and liquidity beta and right now both forces are moving against it at the same time.

Heading into 2025, I expected Bitcoin to see a strong rally driven by growing government support and the rise of AI agents, which seemed poised to reinforce crypto’s network effects and position the space as a high-growth asset class alongside a rerating in software. Despite the rise in stablecoin volumes and its market cap, that optimistic scenario for Bitcoin never materialized.

Instead, as the focus shifted toward agentic AI following developments such as Opus 4.5 and OpenClaw, the dominant narrative became the disruption of software itself. Multiples began repricing rapidly as investors reassessed the durability of traditional software models. That repricing has also pressured private credit, a major source of capital supporting the software ecosystem.

As AI forces a disruptive repricing of software, one of Bitcoin’s key macro identities comes under pressure. At the same time, the broader liquidity cycle is tightening, putting pressure on Bitcoin’s other defining characteristic, its sensitivity to global liquidity.

This is why a crack in private credit is not immediately bullish for Bitcoin. In the short term, it is often the opposite. Bitcoin is liquid, widely held, and easy to sell. In the first phase of market stress, that liquidity matters more than the long-term thesis.

Bitcoin Does Not Front-Run the Panic. It Front-Runs the Rescue.

History supports that sequence.

In the March 2020 dash for cash, Reuters reported that Bitcoin fell more than 20% in a day and more than 30% over five days as investors dumped nearly everything in sight. Then came the policy flood. By January 2021, Bitcoin had risen more than 900% from its March low as governments increased spending to cushion the pandemic shock and investors worried about inflation and currency debasement. Bitcoin did not ignore the panic. It simply repriced the rescue more violently than most other assets.

The same pattern showed up again in the 2023 regional-bank stress. Federal Reserve and inspector-general reviews found that SVB customers withdrew $42 billion in one day, with another $100 billion in requests queued for the next day. Authorities then guaranteed all depositors, and the Fed launched the Bank Term Funding Program, offering loans against eligible collateral valued at par. Following that turmoil, Bitcoin climbed to a nine-month high more than doubling by the end of the year. The pattern is the key: Bitcoin often gets hurt in the scramble for cash, then turns around and monetizes the policy response.

Why the Rescue Is Structurally Inevitable

That dynamic matters even more today because the U.S. system is less able than ever to tolerate a long withdrawal of liquidity.

The Congressional Budget Office said in February 2026 that the federal deficit will be $1.9 trillion in fiscal 2026 and that debt held by the public is already 101% of GDP. At the same time, a widely followed Buffett-indicator tracker put total U.S. market cap at roughly 219% of GDP in early March. That is what financialization looks like: a sovereign already buried in debt, paired with asset markets towering over the underlying economy. In a setup like that, policymakers do not have the room to let every liquidation fully clear on its own terms. The modern economy is too levered to asset prices, and the state is too levered to growth and market functioning, for that kind of purist cleansing to last.

And the Fed has already shown the reflex. It slowed balance-sheet runoff in March 2025, decided in October to end the reduction of securities holdings on December 1, and then began reserve-management purchases in December to maintain an ample level of reserves. Even before a full-scale accident, the system was already moving back toward more liquidity. That matters. Because once you understand that the plumbing itself demands renewed liquidity, you understand why the next real private-credit scare is unlikely to end with policymakers simply folding their arms and watching.

The politics make that even more likely. The SEC’s Investor Advisory Committee said in September 2025 that private-market assets are less transparent and riskier than public-market assets even as access expands through registered structures. Morningstar said semiliquid funds had reached $493 billion in net assets by the third quarter of 2025. Once retail and wealth-channel money is packaged into illiquid credit exposure, private credit stops being a niche institutional issue. It becomes a public problem. And when opaque risk becomes a public problem, the state gets pulled in.

This Is Where Bitcoin’s Original Logic Comes Back

Bitcoin’s white paper proposed a peer-to-peer electronic cash system that would allow payments to move directly from one party to another without going through a financial institution. And the genesis block, not the white paper, famously carried the line, “Chancellor on brink of second bailout for banks.” That distinction matters. The white paper gave the architecture. The genesis block gave the political subtext. Bitcoin was born from a rejection of bailout culture, intermediary dependence, and discretionary rescue. So every time the government steps in to save a fragile system built on hidden leverage, Bitcoin’s original logic gets stronger.

At the same time, the financial rails are moving toward a more always-on world. In October 2025, the Fed said Fedwire and the National Settlement Service would expand to Sundays and weekday holidays, with implementation planned for 2028 or 2029. That is not Bitcoin adoption. But it is the system admitting something important: the economy is becoming more digital, more continuous, and less compatible with old banking-hour assumptions. If AI agents become real economic actors, money and collateral will need to move at software speed. That does not mean every transaction settles in BTC. It does mean that scarce, neutral, digital collateral becomes more relevant, not less.

So the cleanest version of the thesis is this: Buffett’s tide is going out in private credit. AI is exposing the weakest underwriting first, especially where software revenue was mistaken for permanence. Bitcoin got hit in the first wave because it is still trading as both software beta and liquidity beta. But the U.S. is too indebted, the economy is too financialized, and retail is too entangled with private assets for policymakers to tolerate a disorderly unwind for long. The liquidity will come back. And when it does, Bitcoin is usually one of the first assets to understand what that means.

This is why private credit matters so much in the current environment. The irony is that Bitcoin was created for precisely this kind of moment, a world defined by shadow banking, hidden leverage, and governments already burdened with enormous debt loads that limit their ability to respond without creating more liquidity. Private credit is not just another risk bucket in markets. It is where stale marks, embedded leverage, AI-driven disruption, retail packaging, and policy reflexes are now colliding at the same time.

Recent redemption limits and markdowns suggest that this adjustment process may already be beginning. If private credit is where the liquidity tide goes out next, the next major Bitcoin rally will not start with a halving narrative or a clean macro backdrop. It will begin with exposure, followed by a policy response, and ultimately the realization that the system still cannot function without injecting liquidity back into the financial system.

In January 2022, my friend Marko Papic and his strategy team published a report that should have changed how institutional investors think about the next fifty years. It was called “Metaverse Ante Portas”, the Metaverse at the gates, and it was, without exaggeration, one of the most intellectually ambitious pieces of investment research I’ve ever read because of it being from a Geo-Macro strategist about the Metaverse. If you do not follow Marko regularly, you should.

Almost nobody noticed the piece. I have never brought it up to someone and had them say, “I loved that.” Maybe people in crypto were too focused on something else. Or rather, everybody noticed the wrong thing.

The paper dropped at the absolute peak of the crypto and metaverse mania. Bitcoin was already rolling over from its highs. Decentraland and Sandbox, the virtual worlds Marko’s team flagged as investable expressions of the thesis, were about to lose 90% or more of their value. The Goldman Sachs Metaverse index that was recommended would crater alongside everything else in the speculative blowoff of 2022. If you read the paper through the lens of “what should I buy,” you got destroyed. I am sure Marko likes to forget the piece based on the timing but as he knows, I loved it and it has been part of my brain.

But that’s because almost nobody read the paper for what it actually was: a political philosophy thesis disguised as an investment report. And buried inside it was a framework that I believe explains the single most important non-financial consequence of the AI revolution, the crisis of human identity and community that is coming, and what replaces it.

I remember the exact detail in Marko’s paper that planted the seed for everything I’ve been thinking about since. It wasn’t the Benedict Anderson framework or the geopolitical analysis, those came later. It was a throwaway example on page six: Filipinos were earning $300 a month playing Axie Infinity, a blockchain-based game. That was more than the country’s monthly minimum wage of roughly $200. In Venezuela, players were gold-farming in RuneScape and earning more in two days than the $6.70 monthly minimum wage and the activity was essentially untaxable by the state. I referenced Axie Infinity in a recent post and if you have not yet spent an hour with your favorite LLM rabbit holing it, you should.

Marko used these examples to argue for the Metaverse’s commercial TAM. But what I couldn’t stop thinking about was something else entirely. Those players hadn’t just found income. They’d found community. They’d found guilds, teams, and Discord servers full of people who shared their daily reality. They had status within those groups. They had reputation. They had belonging. Their nation-state institutions had failed them, hyperinflation in Venezuela, stagnant wages in the Philippines and they’d rebuilt economic and social identity inside a decentralized game.

That was the moment the thesis started forming for me: what happens when the structures that gave people default meaning, the job, the institution, the nation, start to dissolve? Not just in the developing world, where the collapse is dramatic and visible, but everywhere? When I went back and read the rest of Marko’s paper through that lens, it hit me like a thunderbolt. Not because of the metaverse stuff. Because of Benedict Anderson.

The Manufactured Nation

The intellectual backbone of “Metaverse Ante Portas” wasn’t crypto or VR headsets. It was a 1983 book called Imagined Communities by political scientist Benedict Anderson. Anderson’s thesis, which Marko’s team deployed brilliantly, is that the nation-state is a constructed identity. It’s not natural. It’s manufactured through education, shared language, print media, and institutional infrastructure.

French peasants didn’t wake up one morning feeling French. The state made them French through schools, roads, railways, and a deliberate campaign to replace regional dialects with Parisian vernacular. As historian Eugen Weber documented, this project of turning “peasants into Frenchmen” took decades of intentional infrastructure building.

Marko’s insight was that the machinery of community manufacturing was breaking. The parent-state duopoly over young minds, the partnership between families and institutions that shaped identity for centuries, was being severed by screens, gaming, and virtual worlds. Five-year-olds spending three hours a day on devices, choosing their own content, and interacting with strangers in gaming environments. The state couldn’t manufacture imagined communities if it couldn’t reach the minds of its youngest citizens.

He was right about the diagnosis. He was just early on the vehicle. It wasn’t VR headsets and virtual land parcels that would break the identity-manufacturing machinery. It was AI.

AI as the Identity Solvent

Three years after Marko’s paper, the metaverse is largely a punchline. Facebook’s rebrand to Meta looks like a $10 billion detour. Decentraland is a ghost town. The Goldman Sachs Metaverse basket is a relic. But the forces Marko identified didn’t disappear, they accelerated, powered by a technology nobody in January 2022 was talking about.

AI is doing what Marko predicted the metaverse would do, but faster and more pervasively. Consider the three pillars of nation-state erosion that Marko’s team identified.

First, the subversion of imagined communities. Marko argued that virtual worlds would break the state’s monopoly on identity formation by capturing the attention of children before schools and parents could shape their worldview. AI is doing something even more profound, it is breaking the identity formation of adults. When AI can do the analysis, write the brief, generate the code, and produce the design, the occupational identity that took a lifetime to build becomes unmoored. The nation-state manufactured community through shared institutions. The corporation manufactured community through shared work. AI is dissolving both simultaneously.

Second, the subversion of sovereignty. Marko’s team argued that blockchain protocols would obviate John Locke’s classical liberal premise for the state, that humans organize politically to protect private property. In a world where property is defined by a public ledger, the role of the state diminishes. Today, AI agents operating on stablecoin rails are creating a parallel commercial infrastructure that doesn’t need institutional intermediaries at all. The disintermediation isn’t happening through virtual land deeds. It’s happening through autonomous systems that transact at machine speed, across borders, without asking permission.

Third, the subversion of communication infrastructure. Marko noted that the internet remains a physical construct, fiber optic cables that governments can sever. He pointed to Starlink as a potential game-changer. Three years later, satellite internet has proven its strategic importance in Ukraine and beyond, and decentralized compute networks are distributing processing power across jurisdictions in ways that make state control increasingly difficult.

Every force Marko identified is in motion. The vehicle changed. The destination didn’t.

The Speed of Trust

But here’s what Marko’s paper didn’t fully explore and what I’ve been wrestling with ever since: if the machinery that manufactured community by default is breaking, what replaces it? This is where I think the investment community, including Marko’s analysis, has a blind spot. We are very good at identifying what gets disrupted. We are less good at identifying what gets built in the wreckage.

We have seen this movie before. In the early 1800s, industrialization didn’t just change how things were made, it severed the link between skill and identity. A master craftsman wasn’t just employed. He was somebody. The village blacksmith, the cobbler, the weaver, these weren’t job descriptions; they were social positions. The factory destroyed that. A machine tender was anonymous, interchangeable. The economic logic was overwhelming but the human cost was a generation-long crisis of meaning.

The social consequences were staggering. Alcohol consumption spiked. Family structures buckled. Crime surged in industrial cities. What followed was one of the most remarkable periods of institution-building in human history. Mutual aid societies, trade unions, fraternal organizations, social clubs, and eventually the corporate cultures that defined the twentieth century. The Elks, the Rotary Club, the union hall, the company town. By the early 1900s, America had more civic organizations per capita than any society in history.

Here is what is critical: the economic transition took about twenty years. The social reconstruction took fifty. Technology moved at the speed of capital. Community rebuilding moved at the speed of trust. We are at the front end of that same gap. AI is compressing the economic transition into years, not decades. But the community reconstruction will still move at the speed of trust. That gap between the speed of disruption and the speed of rebuilding is where the crisis lives. This is the dystopian place where we all have to adapt.

The Question: “So, What Do You Do?”

Every cocktail party in America runs on the same protocol. You meet someone, you shake hands, and within ninety seconds comes the question: “So, what do you do?” That question isn’t really about your job. It’s a sorting mechanism, a compression algorithm for identity. It tells us where you fit, how much attention to give you, and whether we share common ground.

AI is about to make that question unanswerable for a lot of people. And what happens next matters more than most investors realize. This isn’t a soft concern. If you think about it the way an infrastructure investor would, the picture clarifies. Every technology transition creates a constraint migration. GPUs were the bottleneck, then it was power, then interconnects, then cooling, then transformer capacity. The bottleneck never disappears, it moves to the next layer.

Human society works the same way. When AI removes the productivity constraint, the bottleneck migrates to meaning. When institutions fragment, the bottleneck migrates to trust. When geographic community dissolves, the bottleneck migrates to belonging. Community is the infrastructure layer that routes all three.

Epistemic Communities as the Template

This brings me back to something hiding in plain sight in Marko’s paper. He described finance professionals as an “epistemic community”, a collective bound by shared beliefs, frameworks, methodologies, and initiation rituals. He used the concept to explain why Wall Street could see crypto but couldn’t see the metaverse: the financial epistemic lens filtered the virtual future into familiar shapes (currencies, yield-farming, DeFi protocols) and rejected the unfamiliar ones (virtual sneakers, digital land, avatar culture).

But epistemic communities aren’t just a cognitive bias. They’re a template. They are exactly the kind of intentional community that replaces default ones when institutional identity breaks down.

The crypto ecosystem figured this out years ago, mostly by accident. When you leave traditional finance to work in an industry that most of your former colleagues think is a scam, you lose access to the old status markers. What replaces them is community, the people who share your conviction, who understand your language, and who validate your choices through shared participation rather than institutional authority. I feel that community as I straddle the traditional macro world I know and the crypto community who has embraced me.

Bitcoin maxis who held through an 80% drawdown weren’t held together by a trade thesis. They were held together by shared conviction about what sound money means for human freedom.

This is why I have been rethinking NFTs lately. When I first mentioned it in a Substack, I was attacked by many in the community. I was surprised. The popular narrative is that NFTs were a speculative bubble that came and went. That is true of the price action. But it completely misses the social function. The most durable NFT projects, the ones that survived the 90% drawdown, survived because they were community tokens, not art. Holding a specific NFT was membership in a specific tribe. It was identity infrastructure. The JPEG was a flag. The Discord server was the town hall.

The New Architecture of Belonging

The communities that matter in a decentralized world won’t look like the ones they replace. They will be organized around three things:

1. Shared Conviction: As AI strips away occupational identity, people will increasingly organize around what they believe rather than what they do. Your tribe won’t be “fellow analysts at Goldman.” It will be people who believe AI should be open-source, or people building sovereign identity infrastructure, or people who think physical communities need to be rebuilt from scratch.

2. Mutual Contribution: The factory gave you a role. The corporation gave you a title. In a decentralized world, your place in a community is determined by what you contribute to it. This is already how open-source communities, DAO contributor networks, and content creator ecosystems work. The answer to “what do you do?” becomes “here is what I have built, here is what I have contributed, here is the community that can vouch for my work.” Contribution replaces employment as the organizing principle of identity.

3. Authentic Connection: This is the one that technology cannot replicate and scales the worst which is exactly why it becomes the most valuable. In a world where AI can simulate expertise, generate content, and automate interaction, the premium on genuine human connection goes vertical.

Conclusion: The Final Bottleneck

The irony is thick. We spent two decades building social networks optimized for engagement and designed for scale and managed to produce the loneliest generation in recorded history. The thing we called “community” online was actually a marketplace for status and outrage. What we need now is the opposite: structures that are small enough to create accountability, durable enough to build trust, and purposeful enough to replace the meaning that work used to provide.

Marko’s paper warned that the Metaverse would sever the sinews tying nations together as imagined communities. He was right, but the word “Metaverse” was the wrong label. What is actually happening is broader and more consequential. AI, crypto, and remote work are simultaneously dissolving occupational identity, institutional belonging, and geographic community.

But if communities can be imagined, if Anderson was right that they are constructed through shared infrastructure, shared language, and shared belief, then they can be re-imagined. They can be built again. Not by the state. Not by the corporation. By people who choose each other.

AI will handle the productivity. Crypto will handle the transactions. But community handles the question that neither technology can answer: Why does any of this matter?

The bottleneck always migrates. And it is migrating to us.

When Breaking Bad ended, most viewers thought the story was over. The empire had risen. The empire had fallen. The chemistry teacher had become the kingpin, and the arc was complete.

Then came Better Call Saul.

Same universe. Same physics. But the camera shifted. Instead of watching the obvious villain build an empire, we followed the lawyer. The system operator. The character who understood the rules and quietly bent them.

The world hadn’t changed.

Our perspective had.

In “The SaaS Panic Is Just the Beginning of a Bigger Story,” I mapped the democratization force, how AI compresses costs, disperses power, destabilizes concentration and ultimately changes the capital structure of the current system.

The panic in software today feels like the end of a story. SaaS multiples compressing. Moats being questioned. Founders rebuilding $50 million ARR products over a weekend with model access and API credits. The death of the software scarcity premium.

That’s the visible drama.

But the more important story, the spinoff arc, isn’t about software.

It’s about equity.

For more than a decade, equity markets were built around a simple premise: durable franchises deserved durable multiples. Investors weren’t just buying earnings. They were buying time. Time to compound. Time before meaningful competition arrived. Time protected by scale, distribution, switching costs, and capital intensity.

Time was the moat.

The entire architecture of modern markets reinforced that belief. Passive flows concentrated into the largest platforms. Growth indices tilted toward scalable digital economics. Valuation frameworks stretched duration assumptions further into the future. A narrow cohort absorbed more and more of the index because the math appeared rational.

Scale begot scale.

But something subtle has changed.

AI does not simply disrupt business models.

It compresses time.

When the replacement cost of competence collapses, when code can be generated instantly and iterated continuously, competitive cycles shrink. A product that once enjoyed a five- to ten-year window of defensibility may now face viable competition in months. Execution speed replaces installed base. Iteration cadence replaces headcount.

And when competitive half-lives shorten, equity changes character.

A share of stock used to represent ownership of a durable franchise with predictable cash flows. In the Age of Agents, it increasingly resembles a call option on execution velocity. Cash flows that once looked like fifteen-year streams begin to look like five-year bets.

When duration compresses, multiples reprice.

This is not simply a SaaS selloff. It is the repricing of time as an asset.

If the last cycle rewarded patience, buy scale, hold duration, let monetary expansion amplify returns, the next may reward adaptability. Velocity over size. Metabolism over moat.

But there is a second-order effect that makes this shift even more destabilizing.

It is not just that duration shortens.

It is that identity becomes unstable.

The End of Fixed Identity

For decades, a moat required stability.

A business needed to be something specific. A CRM platform. A tax advisory firm. A legal services provider. Its defensibility came from clarity. Customers knew what it did, competitors knew what category it dominated, and investors could define its TAM and assign it peers.

Companies grew through hiring specialists, acquiring adjacent businesses, entering new geographies, and layering features onto an existing core. This was bespoke change, but slow bespoke change. It required integration cycles, capital allocation, organizational redesign, and years of execution.

Time created friction. Friction protected incumbents.

But what happens when expansion no longer requires acquisition, hiring, or structural overhaul? What happens when capability can be added at the model layer?

We are watching this in real time.

Anthropic does not “enter” industries in the traditional sense. It releases model upgrades. With each improvement in reasoning, memory, coding ability, or tool use, it suddenly becomes viable across new verticals. One week it threatens internal software development workflows. The next it encroaches on legal research. The next it handles tax summaries. The next it drafts marketing strategy.

It is not acquiring niche firms or building regional offices.

It is simply becoming more capable. And as it becomes more capable, it displaces businesses, one at a time.

This is a new form of adjacency expansion. Historically, adjacency required structural change. Now adjacency emerges as a byproduct of model improvement.

A company once said: “We are a CRM company.”

In the Age of Agents, the framing shifts: We solve X problems—and we can solve adjacent problems tomorrow.

And if that pivot can happen in minutes instead of years, then brand, headcount, installed base, and domain-specific expertise all matter less. Because expertise itself becomes replicable.

That sentence would have sounded absurd a decade ago.

Domain expertise was scarce. It required years of training, credentialing, hiring pipelines, institutional memory. Software companies embedded that expertise into products and wrapped it in recurring revenue.

But when a frontier model can reason across law, accounting, coding, compliance, and marketing, and improve weekly, the scarcity shifts from knowledge to coordination.

The moat was never just about knowing something. It was about owning the time required to know it.

If knowledge can be synthesized instantly, the protective layer weakens. And if capability can be reconfigured dynamically, identity becomes fluid.

Moats depend on predictability. Investors need to believe that what you are today is what you will be tomorrow, only larger. That your category remains intact. That your expertise remains scarce. That your customer relationships remain defensible.

But if companies can reshape themselves rapidly, categories destabilize. And equity markets depend on categories. They rely on sector classifications, peer comparisons, TAM estimates, and long-term competitive positioning. If a model provider can encroach on HR software, then tax software, then legal drafting, then customer support—what is the peer group?

If companies can pivot faster than analysts can reclassify them, valuation frameworks lag reality.

This accelerates duration compression. A moat built on stability supports long-duration cash flows. A moat built on adaptability supports shorter, more dynamic cycles. When firms can morph quickly, competition accelerates. It does not take years to build an adjacent offering. It takes minutes to deploy a new workflow.

The advantage shifts from accumulated expertise to rapid integration. From installed base to execution speed. From identity to iteration.

In a world where companies can become something new overnight, the most durable advantage is no longer what you are.

It is how fast you can become something else.

The Architecture of Velocity

But execution velocity has a bottleneck.

Moats eroded because software got fast. But traditional finance is still slow.

If the new competitive advantage is execution speed, if an AI agent can spin up a marketing campaign, scrape global supply chain data, or rewrite a codebase in seconds, it cannot wait three days for a wire transfer to clear. It cannot wait for layered compliance approvals to move capital across jurisdictions. It cannot operate at scale if every transaction requires human authentication and legacy settlement rails.

The speed of the agent is bottlenecked by the friction of fiat.

This is why crypto is not a parallel narrative to the AI boom.

It is emerging as part of the enabling infrastructure.

To operate at the velocity of machine-native commerce, agents require a financial layer that is programmable, always-on, and globally interoperable. Increasingly, that settlement layer takes the form of tokenized dollars, stablecoins, and eventually Bitcoin-based rails. These systems allow capital to move instantly, deterministically, and without human coordination.

When an agent rents compute, licenses data, accesses APIs, or settles micro-transactions across borders, the most frictionless form of payment is programmable money. Not because it is ideological but because it matches the speed of the underlying intelligence.

This is machine-to-machine commerce.

And over time, it alters the velocity of capital itself. Without a native financial layer, AI remains a very fast brain operating inside a very slow body.

As intelligence accelerates, settlement must accelerate with it.

The Pricing of Reality

If the underlying rails evolve to support velocity, so must the markets that price it.

Traditional equity markets were designed to value decades of stability. Quarterly earnings cycles, forward guidance, sector classifications, discounted cash flow models, this architecture works when competitive landscapes shift slowly.

But what market structure is built to price weeks of execution?

When a company can launch a new vertical in days, when a model upgrade can alter competitive positioning overnight, the quarterly earnings report becomes backward-looking by definition. It reflects what was true. Not what is becoming true.

The Age of Agents requires markets that can price probabilities in real time.

This is where prediction markets enter the conversation.

Prediction markets do not replace equity markets. They complement them. But structurally, they are better suited to pricing discrete events, pivots, product launches, regulatory outcomes, adoption curves, and execution milestones as they unfold.

They price the outcome.

They price the event.

They price the probability of change.

And because many of these markets operate on the same programmable rails that agents use to transact, they increasingly become environments where algorithms, not just humans, participate in price discovery. Agents scrape information, detect mispricings, and execute arbitrage strategies in milliseconds.

They do not “predict” the future.

They process probability faster than humans can update their beliefs.

If equity is transforming into a shorter-duration call option on execution velocity, prediction markets represent a parallel information layer where that velocity is continuously repriced.

They are not a replacement for capital markets.

They are a preview of how real-time probability pricing begins to coexist alongside traditional duration-based valuation.

From Franchises to Call Options

This is where the shift becomes entirely financial.

In the franchise era, volatility was often an opportunity. If the moat was intact, weakness was temporary. The rational strategy was to extend time horizon and add exposure. Patience worked because structural advantage eroded slowly.

In the Age of Agents, volatility may reflect genuine uncertainty about competitive half-life. When barriers fall faster than management can adapt, a drawdown is not always mispricing. It may be duration compression in real time.

Equity therefore behaves differently. It resembles a call option.

A call option’s value depends on execution before expiration. In a world where competitive windows shrink, expiration comes sooner. The embedded optionality becomes more sensitive to velocity.

The paradox is clear.

AI lowers the cost of building businesses. But it raises the bar for sustaining advantage. More companies can start. Fewer can dominate.

That implies greater dispersion. More volatility. Less structural concentration. A market that rewards adaptability rather than mere size.

And it raises the question that follows logically from duration compression: if software moats erode faster, where does durable advantage reconcentrate? The answer may be in the places that resist compression, physical infrastructure, energy constraints, material bottlenecks, regulatory barriers. The assets that cannot be replicated with model access and API credits. The things that still require time.

Equity does not disappear in this world.

It transforms.

From ownership of stability to exposure to speed.

From franchises to call options.

And that is the structural shift beneath the surface panic, the real story unfolding in the Age of Agents.