New Token Economics

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From Ownership to Usage Rights: How Huang Renxun's Formula Reconstructs the Entire AI Industry

He walked onto the stage wearing a leather jacket.

On the screen behind him was a formula.

Revenue = Tokens per Watt × Available Gigawatts.

Applause from the audience.

I stared at both sides of the equals sign and felt something moving.

Not chips, not products, not markets.

It's the coordinate system itself.

A new civilization has just chosen its unit of measurement.

Opening: Transactions of Three Eras

Thirty years ago, Bill Gates sold you a CD.

You took it home and put it on your bookshelf. It's yours forever—if Microsoft goes bankrupt tomorrow, your Windows will still run. Ownership means sovereignty. The asset is in your hands, and no one can take it away.

Fifteen years ago, Marc Benioff told you something else. You don't need to own it, he said. Just pay monthly. The software is in the cloud; access it when you need it, and turn it off when you don't. Simpler, more flexible, and lower upfront investment. What Benioff didn't say was: you'll never finish paying. The meter keeps ticking. Ownership is replaced by a permanent liability disguised as convenience. You trade assets for a monthly bill. Last week, Jensen Huang said something else. He didn't sell you software. He didn't offer a subscription. He stood on a stage in San Jose and presented a formula: Revenue = Tokens per Watt × Available Gigawatts. No product. No license. No number of seats. Only one production equation. Efficiency multiplied by physical capacity. Output is Tokens—the atoms of AI computation, the smallest units of machine-generated intelligence, the basic particles of reasoning that are measured, priced, and industrially produced.

Note what's missing on the right side of the equals sign.

Ownership. There's no word "own" in this formula. No assets. No accumulation. Only production, consumption, and flow.

That's the shift. Not from software to AI, not from on-premises to the cloud. It's a deeper shift: from an economy built on "ownership" to an economy built on "use."

The twentieth century was built on ownership. The token economy ended it.

And this will change everything that's priced in the old units—almost everything now.

Part One: The Death of Ownership Economics

I. Three Pillars Collapse One by One

Ownership economics is built on three premises, each so natural, so ancient, that we've long since stopped noticing they are premises.

The first pillar: You own your tools. Software is a capital asset. You buy a license, depreciate it over three years, and own the productivity it represents. Enterprise software is a moat—not just because of migration costs, but because ownership itself is a permanent claim. "We have SAP" means something: investment, commitment, infrastructure that outlives any employee. In the token economy, this pillar isn't bent, it's broken. You don't buy an AI agent. You invoke it. You consume tokens to initiate its inference, complete tasks, and receive output. When the task is finished, the relationship ends. There are no assets on your balance sheet, only a consumption record. The agent that completed 10,000 tasks for you last quarter is, in an accounting sense, exactly the same as an agent you never used. The moment you stop paying, the capability disappears. Not because the contract expired—because nothing was yours. The tool doesn't belong to you. Never. You rented a capability with tokens, and it's gone when you're done. The second pillar: You own your data. "Data is the new oil" was the defining metaphor of the 2010s. Companies spent billions accumulating proprietary datasets, training their models, and building data moats that competitors could only replicate over years. The logic was impeccable: accumulate raw materials, and you control production. But the inference era changed the value equation of existing data in a way that was hardly ever clearly discussed. In the training era, historical data was everything. The quantity and quality of datasets determined the upper limit of a model's capabilities. Owning data was owning a direct agent of intelligence. In the inference era—the era that Jensen Huang declared had arrived decisively—value computing shifted. Real-time inference on fresh contexts often outperforms pattern matching on stale historical data. An agent capable of real-time searching, synthesizing, and inferring often outperforms a model trained on a proprietary database from the previous year. Accumulated advantages are eroding. Inference efficiency advantage is dominant. This doesn't mean data becomes worthless. It means the relationship between "owning" data and "owning" intelligence is no longer linear. You can have trillions of bytes of proprietary data and still lose to a competitor with a more efficient Token/Watt and a more accurate inference stack. The moat isn't data. The moat is the assumption that data accumulation is irreversible. That assumption is now being questioned. The third pillar: You own your model. For a few years, training a cutting-edge model was the ultimate expression of ownership economics applied to AI. Spend hundreds of millions of dollars, assemble a world-class research team, collect proprietary data, run training on thousands of GPUs—ultimately, you own something no one else has. An asset. A competitive weapon. Yours. The way this pillar collapses is more subtle than the other two, and it's where most analysts fall short. The argument is not that models are unimportant. Cutting-edge models—Claude, GPT-4, Gemini Ultra, top-tier inference systems—still represent real capability differences and can still support real pricing power. When you need a system that can infer in a context of 200,000 tokens, maintain logical coherence in multi-hour agent workflows, and generate outputs that senior analysts are willing to sign, cutting-edge models are not commodities. You pay a premium because the cost of failure is too high, and cutting-edge models fail less. More specifically: Intermediate-tier models are dying. Not cutting-edge models. Not small open-source models. Intermediate-tier. Models that have enough capability to feel like a real product, but not enough capability to support cutting-edge pricing. Too costly to perform large-scale commodity inference; too weak to sign cutting-edge contracts. Compressed from both ends. In the era of usage rights, sufficiency cannot create a tokens/watt advantage. It can only create a pricing squeeze coming from both directions simultaneously. Model capabilities have transformed from a moat into an admission ticket. The middle layer paid the admission fee, only to find there were no seats for them in the arena. II. What Does the Formula Really Say? Returning to Jensen Huang's equation, it deserves a closer look than the media has given it. Revenue = Tokens per Watt × Available Gigawatts Financial media interpret it as demand forecasting, and they're not wrong—this is Nvidia's argument: as global power capacity expands and AI factories are built, revenue grows proportionally to token production efficiency. More gigawatts, more tokens, more revenue. Clean industrial logic. But this formula contains a philosophical statement that has been almost unexamined. Jensen Huang chose to measure output in tokens. Not the number of model calls, not the number of API requests, not "AI interactions"—it's tokens, the atomic units that generate intelligence. He chose to measure efficiency in watts. Not the cost per query, not latency—it's watts, the raw energy consumed. The implicit claim: Intelligence is a manufactured commodity. It's produced in the same way electricity is produced, as steel is produced. Raw materials (energy) go in, outputs (Tokens) come out. The ratio between the two—Tokens per Watt—is the fundamental measure of competitive advantage. This is proof of the death of the software-age belief: Intelligence is primarily an information problem. It isn't. It's a manufacturing problem. The question isn't "Who has the best algorithm?" The question is "Who can produce the most reasoning with the fewest joules?" But what the formula doesn't say—this omission is crucial—is: Whose intent is being served? Tokens are produced. Tokens are consumed. Revenue is created. The equation balances. But it doesn't ask anywhere: What do users really want? Is the intent behind token consumption clear? Is the output worth the electricity? Does the person at the other end of the reasoning chain get what they came looking for? This formula describes the supply side of the smart economy with remarkable precision. It completely misses the demand side. This is the gap. And this gap is the real argument of this article. We'll come back here. Part Two: New Rules of the Economics of Use Rights III. Three Rules to Replace the Old Logic The economics of use rights is not just a new pricing model. It's a different set of rules of competition—a different set of capabilities, different moats, and different organizational structures that favor the economics of ownership. Rule One: Pay for flow, not for ownership. In the economics of ownership, the relationship between buyer and seller is fundamentally about transfer. Money flows in one direction, assets flow in another. The transaction is complete, and the relationship ends in principle. You own that thing. The seller gets paid. End. In the economics of usage rights, relationships never end. Every token consumed is a transaction. The meter keeps running. The more you use it, the more you pay—the more value you extract, the more value the provider captures. This isn't buying and selling; it's a perpetual exchange. This has a profound impact on how companies structure themselves. In the SaaS era, enterprise software companies were "transfer machines"—moving licenses from their own inventory to their customers' balance sheets. In the token era, they become "traffic machines"—needing to maintain and scale the rate at which tokens are consumed. Revenue isn't a function of the number of customers, but of how many tokens those customers consume. Growth, in this model, isn't like signing new contracts. It's like deepening the usage of existing accounts. The question shifts from "How do we close this deal?" to "How do we increase traffic?" Rule Two: Efficiency is the new moat. In the era of ownership, the most defensible competitive position is built on accumulation: accumulating data, accumulating customer relationships, accumulating migration costs. The longer you stay, the harder it is to leave. Network effects reinforce ownership advantages. The rich get richer because they have more. In the economics of usage rights, the most defensible competitive position is efficiency: the ability to produce more tokens per watt with lower latency and higher reliability. This is Nvidia's entire bet. The company that can produce the most intelligence with the fewest joules will be able to offer the lowest prices with the highest profit margins—or, depending on market segmentation, the highest prices with competitive profit margins. Tokens/Watt is not an engineering metric residing in a data center operations spreadsheet. It is a business model metric. It determines who can profitably serve the large, low-margin market of token commodities while also serving the small, high-margin market of cutting-edge inference. It determines who is squeezed out and who survives when token prices fall—and they inevitably will. The moat is no longer what you accumulate. The moat is the efficiency with which you convert energy into intelligence. Rule Three: Scheduling Capability Replaces Accumulation Capability. This is perhaps the deepest shift in rules. In ownership economics, strategic advantage accumulates for those who can accumulate the most—the most data, the most talent, the most computing power, the most customers. Accumulation is the game itself. In usage economics, strategic advantage accumulates for those who can allocate resources most effectively. The question isn't "How much do you have?" but "How intelligently can you deploy your existing resources?" This applies at every level. At the infrastructure level: Who can schedule heterogeneous computing power across GPU types, cooling systems, and network topologies to maximize Tokens/Watt? At the software level: Who can schedule inference jobs to maximize throughput while minimizing latency? At the individual level: Who can guide the AI ​​Agent with sufficiently clear intent to extract the maximum value from the Token budget? The word "scheduling" deserves emphasis. An orchestra doesn't own music. It doesn't manufacture instruments. What it does—what it irreplaceably does—is translate the composer's intentions into harmonious sound. The value of a conductor lies not in what they have, but in what they can make happen. This is the new competitive landscape. It chooses capabilities drastically different from the old one. IV. The Fundamental Shift in the Competitive Axis

The left column describes the game that most large tech companies have been playing for the past two decades. They are very good at it. They have built organizations, incentive structures, acquisition strategies, and engineering cultures optimized for it.

The right column describes the game that almost no large tech companies have played. The required skills are different. The metrics are different. The winning organizational structures are different.

This is why the token economy is truly disruptive—not because it makes existing products obsolete (although it will), but because it makes existing organizational capabilities obsolete.

This is why the token economy is truly disruptive—not because it makes existing products obsolete (although it will), but because it makes existing organizational capabilities obsolete.

World-class companies, having accumulated all their advantages, are starting from scratch, with these advantages subtly out of sync with the new rules. This transition isn't happening in ten years. It's happening now. Part Three: Winners and Losers Fifth and Fourth Types of Winners In any systemic change, the first question is: Who sets the new rules that benefit them? Winner ①: Energy and Thermal Infrastructure The token economy, at its physical foundation, is an energy economy. Tokens need electricity. More tokens need more electricity. Better tokens—lower latency, higher throughput—not only need more electricity, but also better electricity: more precise delivery, more efficient cooling, and more reliable allocation. Companies like Vertiv, providing thermal management and power systems for high-density data centers, are experiencing something unparalleled in the software age: they are key inputs to intelligent manufacturing. In ownership economics, cooling systems are cost centers. In the token economy, they are production infrastructure. This distinction makes sense for valuation. As AI factories push rack densities to 150 kilowatts—compared to 10-15 kilowatts in traditional data centers—liquid cooling systems become non-negotiable. Not a luxury feature, but an operational prerequisite. Vertiv's backlog of over $15 billion isn't a sales achievement; it's a measure of how quickly the physical infrastructure of the token economy needs to expand. This is the structurally safest position in the entire AI value chain. Vertiv doesn't care which AI model wins. It doesn't care which cloud provider dominates. It cares that AI factories are built and operated at ever-increasing density. This trend has at least a decade of runway ahead. Winner ②: The Advanced Chip Manufacturing Monopolist If Tokens/Watt is the fundamental competitive metric in the token economy, then the entity that controls the physical upper limit of Tokens/Watt performance wields extraordinary structural power. This upper limit is determined by semiconductor physics—how many transistors can be crammed into each square millimeter of silicon, and how efficiently these transistors can switch. Today, this upper limit is controlled by TSMC, whose 2-nanometer process represents the current cutting edge allowed by physics and manufacturing precision. TSMC's capacity at its most advanced nodes, literally, represents the global production capacity of the smart economy. It cannot be quickly replicated. Capital costs are in the tens of billions. Process know-how takes decades to accumulate. Supplier relationships, equipment, cleanroom specifications—each represents a composite advantage that no competitor can match in terms of scale. Jensen Huang's demand forecast of $1 trillion by 2027 is essentially a TSMC capacity constraint issue. The demand exists. The question is how quickly the physical supply chain can expand to meet it. TSMC's position in this dynamic is not that of a traditional supplier, but rather a natural monopolist in the most critical input of the fastest-growing economic activity on Earth. Winner ③: Token Scheduling Software Layer Sitting between the physical infrastructure and the actual work is the scheduling layer: the software that determines how inference jobs are scheduled, how computing resources are allocated, and how latency and throughput tradeoffs are managed in real time. Nvidia's Dynamo—an operating system designed specifically for AI factories—represents its attempt to possess this layer. The logic is straightforward: if Nvidia controls not only the hardware but also the software that schedules the hardware, it captures value on two levels simultaneously. Hardware revenue comes from chips. Software revenue comes from the scheduling layer. The combination of the two: better scheduling software makes Nvidia hardware perform better on the Tokens/Watt metric, making Nvidia hardware more attractive to buy. This is the same vertical integration logic Apple applies to PCs and smartphones. Control the metal and software stack. The gap between "our system" and "everyone else's system" widens with each generation. Companies that can build an effective scheduling layer—whether it's Nvidia's Dynamo, a specialized inference optimization company, or a cloud service provider developing proprietary scheduling systems—will control the profit structure of the token economy in ways that pure hardware providers cannot. Scheduling is where intelligent production efficiency translates into business model advantages. Winner 4: Sovereign AI Infrastructure Builders There is a fourth type of winner that hasn't received the analytical attention it deserves: builders of sovereign AI infrastructure. Every country that concludes—that it cannot rely on foreign token production capabilities—becomes a customer of the entire AI factory stack: chips, cooling, networks, scheduling software, basic models, everything. This is not a consumer market. This is a government procurement market, with the budget size, political priorities, and timeline stability inherent in government procurement. Demand is structural. It doesn't depend on quarterly performance or consumer behavior. It depends on geopolitical decisions that, once made, tend to persist across political cycles. The token economy, in this dimension, is not just a commercial revolution. It is becoming a geopolitical revolution. Every government that wants to produce tokens domestically is a long-term customer of companies capable of building and operating national-scale AI factories. VI. Four Types of Losers Naming losers during systemic change is uncomfortable, but it's a necessary analysis. Discomfort is not a reason for avoidance.

Loser ①: Traditional SaaS Pricing Model

The per-user, per-month subscription seat model—regardless of how much each user actually does—was elegant in the pre-AI era. Predictable. Easy to budget. Aligns vendor incentives with customer retention.

In the AI ​​era, it has an inherent paradox that becomes sharper with each improvement in AI capabilities. The more powerful the AI ​​agent, the more a single user can accomplish with fewer human actions. As AI takes over more workflows, the link between "number of users" and "value extracted" decouples. Companies heavily utilizing AI may extract five times more value from a software platform while only needing half the seats because AI handles the other half of the work.

This is good for customers. For per-seat SaaS vendors, it's a matter of survival. The delivered value increases, but the pricing mechanism doesn't capture any of that increase.

At GTC, Jensen Huang said, "Every SaaS company will become an Agent-as-a-Service company." This isn't a prediction; it's an observation about survival. Providers who figure out how to price by token consumption, by results, and by the value of delivery—not by occupied seats—will survive the transition. Those who continue to defend seat pricing because their financial models rely on it will experience a slow, structural revenue leak, which, from the inside, looks like a customer success problem. The transition window isn't infinite. Companies that have already shifted to usage-based pricing have a compound advantage. Those still debating whether to make the change are consuming their transition window. Loser ②: Cloud service providers with low token efficiency. Token/Dollar is becoming the new benchmark for cloud AI services. It's not just about latency, not just about raw throughput. It's about the ratio: how much useful AI output do you get for every dollar you spend on infrastructure? Cloud service providers with older hardware generations, less optimized thermal infrastructure, or less sophisticated scheduling software will find themselves systematically underperforming on this metric. In a commodity market—where the mass end of token production is becoming a commodity market—systematic underperformance on key metrics is a pricing problem that compoundes over time. Mid-sized cloud service providers that cannot justify capital investment to stay ahead of the Tokens/Watt efficiency curve face a structural squeeze: their token production cost base is higher than that of their leading competitors, forcing them to compress profit margins or lose customers to cheaper alternatives. Neither path looks good. Loser ③: Hoarding Knowledge Workers This is harder to write because it describes a truly struggling class of professionals. But precision requires clear articulation. Knowledge work in the ownership era rewards accumulation. Accumulate expertise. Accumulate relationships. Accumulate institutional knowledge. Professionals with two decades of experience in the industry—who understand regulations, key figures, historical context, and unwritten rules—have a structural advantage over any newcomer. Their accumulated capital isn't on the balance sheet, but it's real. The token economy erodes this advantage in a specific way. The majority of the capital that constitutes a professional's accumulated capital—information gathering, document analysis skills, report synthesis, and communication drafting skills—is now tokenizable. An agent with well-designed prompts and proper database access can accomplish these tasks at a speed and a fraction of the cost that humans cannot sustain. This doesn't mean accumulated expertise becomes worthless. It means the type of expertise that survives in the token economy looks different. Knowledge workers who can guide AI agents with high clarity of intent—those who can channel token consumption toward valuable results, and those who can evaluate AI output with genuine domain judgment—retain and potentially amplify their value. Knowledge workers whose primary value lies in information gathering, data processing, or routine analysis face a real structural shift. The important distinction isn't "using AI versus not using AI." It's: **Are you consuming tokens, or are you channeling tokens?** Consumers are replaced. Schedulers become more valuable. Loser 4: The Middle Tier Model As established in Part 1: Not the model as a whole. It's the middle tier. Frontier models retain pricing power because they can do things that nothing else can reliably do. Complex multi-step reasoning, long contextual coherence, and truly fuzzy judgments. Customers pay a premium because the cost of failure is too high, while frontier models fail less. Open-source small models retain viability because their tokens/wat efficiency is extremely high. Local deployment, no API cost, and extremely fast reasoning for narrow, well-defined tasks. Even with moderate capabilities, the economics hold true at scale. The middle tier—models that have enough capability to feel like a real product but not enough capability to support frontier use cases or enough efficiency to support commodity deployment—is stuck. It can't win by capability, and it can't win by efficiency. It competes with inertia and existing relationships, both of which are eroding. Model capability has become an entry ticket, not a moat. An entry ticket is not an asset. You pay once and are allowed in. It doesn't accumulate for you. Part Four: Deep Restructuring VII. The Salary Revolution Jensen Huang said something at GTC that received far less attention than his hardware announcements, but may have more implications for how the economy will actually operate five years from now. He said that every engineer at Nvidia will eventually receive an annual Token budget—worth about half their cash salary—on top of their base salary, specifically for deploying AI Agents as productivity multipliers. "I'll give them about half of their base salary as Tokens," he said, "so their productivity can be amplified tenfold." This isn't a welfare announcement. This is a new theory about labor. In an ownership economy, employers buy workers' time. Wages are the price of an hour, implicitly understood as the employer directing what happens within those hours. Time is the unit of labor. Wages are the price of time. In the token economy, the equation changes. Workers still sell their time—their presence, judgment, and domain knowledge. But they now also receive a budget for intelligent production capacity: a token quota representing the ability to run an AI Agent, generate analytics, draft output, and process information at a rate no human could sustain. The new labor formula is roughly: Output = Intent Clarity × Token Configuration × AI Efficiency. Note what this formula does. It makes an individual's value a function, not just their time, but how effectively they can guide the AI ​​Agent. The only variable controlled by humans in this formula—and the only one not purely a function of infrastructure—is intent clarity. Knowing what you want to accomplish, specifying it precisely enough for an agent to execute, and evaluating the output based on true intent rather than literal instructions. This is the ability to reprice upwards in a token economy. Not execution, not information gathering, not routine analysis. The ability to have clear, valuable intent—and the ability to translate that intent into effective agent scheduling. For every knowledge worker, the question to sit in their head now is: What parts of my work can be done adequately or better by token-consuming agents? What remained after that audit were valuable professional assets worth developing. What appeared on that list were risk exposures that needed to be managed. IX. Intent: The Only Thing That Cannot Be Rented Three times in economic history, the resources that civilizations fought over—resources whose control determined power, wealth, and strategic advantage—have shifted. The Industrial Revolution made capital the key resource. Machines, factories, railways—whoever owned the production equipment owned the economy. Capital can be accumulated, inherited, and deployed at scale. The great wealth of the nineteenth century was the wealth of accumulated capital. The internet age has made time—specifically, human attention—a critical resource. Whoever can capture and guide human attention on a massive scale can build the platform commerce that dominates the early 21st century. Time can be structured, monetized, and sold to advertisers. The greatest wealth of the early digital age was the wealth of accumulated attention. The token economy makes intention a critical resource. It's not capability. It's not data. It's not computing power—computing power is infrastructure, not a differentiating factor. It's intention: the clarity of what you want to accomplish, the precision with which you can specify it, and the wisdom to know what is worth wanting. This is the paradox at the heart of the use-rights economy. In the use-rights economy, almost everything can be rented. Computing power can be rented by tokens. Storage can be rented by quadrillion bytes. Intelligence can be rented by reasoning. Models can be rented by API calls. You can rent a cutting-edge inference system, a code generation agent, a research assistant, a document analyzer. You can assemble capabilities that would have required a team of experts ten years ago, all with a monthly token budget. Almost everything can be rented. Almost everything—except intent. Intent cannot be rented because it is not fundamentally a capability. It is not something that can be produced by a model or expressed by a formula. Intent is the a priori condition that makes all capabilities meaningful. It is the direction before movement, the question before the answer, the purpose before the tool. An agent that consumes 10,000 tokens and produces meaningless output, no matter how efficient it is, creates nothing of value. An agent that consumes one hundred tokens to produce an output perfectly serving a clearly understood purpose has done extraordinary work. The difference between these two scenarios is not model quality, nor infrastructure efficiency, but the clarity and quality of the human intent initiating the token consumption. This is why Jensen Huang's formula, while precise, is incomplete. Revenue = Tokens per Watt × Available Gigawatts This formula describes the supply side of the smart economy with clarity. It makes no mention of whether the intelligence being produced is worth producing. The complete formula—the one that captures both sides of the ledger—is roughly: Value = Intent Clarity × Token Configuration × Available Computing Power. The first variable is the one that no infrastructure investment can increase. Nvidia can build better GPUs. TSMC can develop more advanced process nodes. The scheduling layer can become more sophisticated. All these improvements increase the efficiency with which intent is served. But the intent itself must come from somewhere. From someone. From someone who truly understands what matters—what matters, why, and what constitutes success. This, ultimately, is something worth developing. It's not about token-consuming skills, nor is it about prompting engineering as a mechanical process. It's about a deeper ability: knowing clearly enough to know what you want so that the agent can execute it; and distinguishing between output that truly serves the intention and output that merely appears to. In a world where almost every ability can be rented, the scarcest and most valuable thing is knowing why you're renting it. Not A, but B. Not having more computing power, but knowing what to do with it. Not accumulating more tokens, but knowing why to allocate them. This is the only true ownership in the economics of usage rights: the intention is always your own.

End: Back to the Formula

He left the stage.

The formula remained on the screen.

Revenue = Tokens per Watt × Available Gigawatts

I stared at it and thought: This is an equation about production.

Precise, powerful, physical. It tells you how AI factories operate, where the competitive axis is, and where capital will flow in the next decade. What it doesn't tell you is the person at the beginning of the chain. Before a token is produced, someone decides to produce it. Before a deduction is run, someone determines a question is worth asking. Before an AI factory converts electricity into intelligence, a purposeful person initiates the process. The formula describes the transformation. It doesn't describe the starting conditions. Thirty years ago, the question was: What do you own? Fifteen years ago, the question was: What do you subscribe to? Today, the question is: How many tokens can you dispatch? But the question beneath all these questions—the one the formula doesn't ask, the one the infrastructure can't answer—is older and simpler: What do you really want? Not what you can produce. Not how efficiently you produce it. What is the intent behind initiating the chain? What is worth spending tokens for? The complete formula: Value = Intent Clarity × Token Configuration × Available Computing Power Nvidia, TSMC, and Vertiv, along with every AI factory on every continent, can improve the last two variables. They are doing so at an extraordinary speed and on an extraordinary scale, and the result will reshape the physical infrastructure of civilization. The first variable is yours.

The token economy gives everyone access to extraordinary abilities. It doesn't give anyone clarity on how to use them. It makes production cheap. It doesn't make wisdom cheap.

In a world where almost every ability can be rented out as tokens, the scarcest thing is knowing why you're renting it.

Huang's formula describes the world that is becoming.

The important formula is the one that describes what you become in it.

Tokens serve intentions.

And intentions—always, still, irreducibly—are your own.

That's all.