This is the fifth paper in a five-part sequence about artificial intelligence. Can Software Cheat? separates software behavior from consciousness and responsibility. Is Artificial Intelligence Biased, or Is It Answering From a Point of View? examines perspective and judgment. What Are We Actually Afraid Artificial Intelligence Will Do? separates present harms, plausible risks, and speculative catastrophes by mechanism and evidence. Compounding Intelligence explains how people and organizations can build durable advantage with these tools. This paper moves outward to the physical, financial, and institutional system that makes those gains possible.
TL;DR
Artificial intelligence (AI) may arrive through a screen, but an industrial system builds it. Capital and electricity move through semiconductor equipment, fabrication, packaging, memory, networking, data centers, cloud platforms, models, applications, and human organizations before becoming useful intelligence. That system has genuine points of fragility, but its interdependence also creates strong incentives to solve constraints. Competition can weaken the economics of particular companies while strengthening the system as a whole. Falling costs may increase total demand rather than reduce it. The system can therefore become economically important even if some companies, projects, and investors fail. The right unit of analysis is not the model, the chip, or the data center alone. It is the complete system, including the people who finance, build, regulate, deploy, and use it.
Software never stopped being physical
Over five decades in software, I watched abstraction hide more and more machinery. Developers moved from controlling individual bytes and devices to calling libraries, services, and application programming interfaces. Business users moved from specialized terminals to personal computers, browsers, mobile devices, and cloud applications. Each generation made an expanding technical system feel simpler.
That was the point. The best software systems hide complexity so that people can concentrate on the problem they are trying to solve.
AI takes this abstraction much further. A user can ask an ordinary-language question and receive an answer assembled by a system whose physical and organizational complexity is almost entirely invisible. The interaction may feel weightless, but the system behind it is not.
A graphics processing unit (GPU) order is not an AI service. The processor must be designed, fabricated, packaged, supplied with high-bandwidth memory (HBM), connected to other processors, installed in data-center servers, cooled, powered, financed, programmed, and incorporated into a model. That model must then be delivered through a cloud service or application, connected to useful information and workflows, governed by an organization, and used by a person with a purpose.
AI is a physical industrial system disguised as software.
This distinction changes how we evaluate both opportunity and risk. A surprising model demonstration tells us little about how quickly reliable capacity can be manufactured, energized, deployed, and converted into economic value. Conversely, a delay at one data center does not mean the wider system has failed. The unit of analysis must be large enough to include the dependencies and specific enough to identify where the constraint has moved.
The system has five connected layers
The full system is complicated, but it becomes manageable when grouped into five layers.
Physical inputs include electricity, land, water or alternative cooling, raw materials, buildings, construction, and transmission capacity.
Compute infrastructure includes semiconductor equipment, fabrication, advanced packaging, processors, memory, networking, servers, storage, and data centers.
Platforms include cloud services, foundation models, development tools, security, evaluation systems, and model-routing software.
Applications and organizations turn technical capability into completed work through workflows, proprietary information, domain expertise, controls, and human judgment.
Capital and institutions surround every layer through financing, insurance, regulation, permits, standards, education, and public acceptance.
The layers are not a simple assembly line. They are a feedback system. Better models increase demand for computing. Greater demand attracts capital. Capital funds manufacturing and infrastructure. Additional capacity lowers cost and expands access. Broader use reveals new applications and new constraints, beginning another cycle.
This resembles Amazon’s long development arc. Retail growth that originally focused on a few books grew to require warehouses, logistics, software, data, operating disciplines, and large amounts of capital. Capabilities built for Amazon’s own use later became infrastructure for other businesses, most visibly through Amazon Web Services (AWS). AI is following a similar pattern at a much larger physical scale. Internal tools, data pipelines, evaluation methods, security controls, and trained people can become reusable infrastructure for the next application.
The earlier paper Compounding Intelligence describes this effect within a person or organization. This paper describes the industrial system that supplies the capacity on which that compounding depends.
Bottlenecks move
Every major system has constraints. The mistake is to assume that today’s constraint will remain the decisive one.
At one point, leading-edge processors appeared to be the central scarcity. More processors increased demand for high-bandwidth memory, advanced packaging, network switches, optical links, servers, cooling equipment, transformers, turbines, electricians, and grid connections. Solving one bottleneck raised the value of the next scarce input.
Some constraints are harder to replace than others. I use the word lynchpin for a component, capability, or organization whose prolonged loss would stop or materially delay the system because no substitute could match the required quality, scale, cost, and speed.
ASML Holding N.V. (ASML) is the clearest equipment example. Its extreme-ultraviolet lithography systems are essential to manufacturing the most advanced chips, and no competitor can presently supply an equivalent system at commercial scale. Existing machines would continue operating if new deliveries stopped, but maintenance, spare parts, upgrades, new capacity, and progress to later manufacturing generations would become increasingly constrained. ASML is therefore a technology-progress and future-capacity lynchpin, not an instant on-off switch. [3]
Taiwan Semiconductor Manufacturing Company (TSMC) is a manufacturing lynchpin. Its importance comes from leading-edge fabrication, yield, scale, advanced packaging, design support, and a broad customer ecosystem. Competing chip architectures can all depend on the same foundry. A major interruption would therefore affect many ostensibly independent companies at once. [4]
Other system components are deployment-rate constraints rather than system-essential lynchpins. Alternative suppliers or locations exist, but they cannot be added quickly or cheaply enough to preserve the original schedule. High-bandwidth memory, networking equipment, turbines, transformers, switchgear, cooling, skilled construction labor, and grid connections often fall into this category.
This distinction matters. A lynchpin can threaten the system’s technical path. A deployment constraint usually changes timing, cost, geography, and who captures the profit. Treating every shortage as an existential threat obscures the more practical question: what will the system do next?
Interdependence can create commitment
Complexity is often described only as fragility. That is half the story.
My work in investment banking and transaction due diligence trained me to look beyond a project’s promoter. I want to know who the counterparties are, what they have committed, how much they can lose, whether contracts are enforceable, and whether the participants have the technical and financial capacity to solve a problem when the plan changes. Promotional claims are cheap, but signed obligations by capable parties are not.
The AI buildout involves some of the world’s largest technology companies, semiconductor manufacturers, infrastructure managers, banks, utilities, contractors, sovereign investors, and institutional customers. Their participation does not prove that demand forecasts are correct or that every project will earn an acceptable return; capital has made spectacular mistakes before. It does show that the system is supported by parties with money, customers, technical staff, political influence, and strong incentives to remove obstacles.
I call this network commitment. It differs from common-mode dependency, in which many apparent participants ultimately rely on the same weak customer, financing source, technology, or assumption. A well-supported network can adapt when one route closes. A common-mode dependency can fail in several places at once because the apparent diversity was illusory.
This distinction matters especially in ecosystem financing. A supplier may invest in a customer, guarantee capacity, extend credit, or help assemble a project. Those arrangements can solve a legitimate coordination problem. They can also conceal demand that exists mainly because the supplier financed the purchase.
The useful questions are concrete. Is capacity being used? Are independent customers producing enough cash to pay for it? Does the contract last as long as the financed asset? Can the asset serve another customer? Who owns the downside if utilization disappoints? The label circular financing answers none of these on its own.
Demand should be measured in useful work
Tokens are useful for operating a model, but they are a weak measure of economic demand. A business does not ultimately want tokens. It wants a support case resolved, a deal room analyzed, working software produced, a molecule designed, a claim reviewed, a sales lead qualified, or a workflow completed.
The better unit is a useful task. Its measurement should include completion quality, time saved, human review required, error cost, latency, energy use, and economic value.
Current use is already large. OpenAI reported in September 2026 that its products reached more than one billion weekly active users and 2.5 million businesses. Anthropic reported in May that its run-rate revenue had crossed $47 billion. Microsoft Corporation (Microsoft) reported more than 30 million paid Microsoft 365 Copilot seats in its fiscal year 2026. These company-reported measures of AI use and revenue show that demand is no longer confined to demonstrations and experiments. [5][6][7]
Raw adoption still does not establish economic value. A free consumer question and a production workflow are different kinds of demand. A pilot and a renewal are different. An announced contract and recognized revenue are different. The relevant evidence is whether use deepens, customers renew, useful tasks become cheaper, and operating value supports the infrastructure being built.
This is another place where simple counts can mislead. The denominator is not the number of people on Earth. Agentic systems allow one person or organization to launch many machine-directed tasks at once. The practical ceiling is the number of useful tasks that can be performed economically, with acceptable supervision and risk.
Efficiency can increase total resource use
Many forecasts assume that more efficient chips and models will reduce the need for computing and electricity. The cost of a single task should fall. Total demand may still rise.
The Jevons effect occurs when an efficiency improvement lowers the cost of using a resource enough that people use much more of it. A more capable and less expensive AI system can attract more users, handle more kinds of work, run longer tasks, support more simultaneous agents, and make previously uneconomic applications viable. The amount of computing required for each task can decline while total computing grows.
This is not merely theoretical. OpenAI has reported that individual use deepens over time, with message volume and task variety increasing after adoption; my changing usage patterns are a good example of this. The International Energy Agency (IEA) projects that global data-center electricity consumption will rise from about 485 terawatt-hours in 2025 to about 950 terawatt-hours in 2030, roughly 3 percent of global electricity demand. AI-focused data centers grow faster within that total. [1][5]
The projection is uncertain, but the mechanism is important. Efficiency is not the opposite of demand. Efficiency can unlock demand.
That insight also challenges zero-sum assumptions about resources. Local electricity capacity is finite at any given moment, and poorly planned growth can raise costs or delay other projects. The energy system itself is not a fixed pie. Generation, transmission, storage, demand flexibility, siting, and efficiency can expand. The real questions concern timing, investment, allocation, local effects, and who bears the cost.
Energy will determine pace and geography
Semiconductors are globally traded. Electricity is local. A chip can cross an ocean; a grid connection cannot.
The International Energy Agency projects that data-center electricity use will roughly double worldwide by 2030. A June 2026 update from Berkeley Lab estimates U.S. data centers used 4.7 percent of electricity in 2024 and projects 9.5 to 15.3 percent by 2030, with 11.8 percent in its reference case. The range is wide because model efficiency, chip efficiency, utilization, deployment rates, and workload growth remain uncertain. [1][2]
Power is therefore the least predictable layer, but unpredictability should not be confused with impossibility. Developers can combine existing grid generation, new generation, nuclear restarts and uprates, natural gas, renewable power, storage, fuel cells, microgrids, demand flexibility, behind-the-meter systems, and improved computing efficiency; many examples of these approaches crossed my desk as an investment banker. Not every approach will work everywhere, but a mix can work in enough places.
The likely result is uneven development. Regions with power, fiber, land, skilled labor, political support, and workable permitting will move faster. Constrained regions will delay projects, charge more, or lose them. This shifts value toward energized sites, electrical equipment, service providers, and jurisdictions able to execute.
It also creates legitimate public questions. Who pays for new transmission and generation? Are residential customers subsidizing large loads? How much water is required, and what alternatives exist? What tax incentives are granted? How many lasting local jobs result? Can a data center reduce load during grid emergencies? These are infrastructure-governance questions, not evidence that computation itself is illegitimate.
These local tradeoffs warrant a separate discussion. My narrower point is that energy constrains the rate and location of AI deployment without imposing one fixed global ceiling.
Competition can weaken companies while strengthening the system
Technology discussions often search for a single winner. Software developers learned to choose a platform, build around it, and avoid fragmentation. The industrial system is less tidy.
NVIDIA Corporation (NVIDIA) occupies a broad position across processors, networking, software, reference architectures, and complete systems. Its dominance does not mean that Advanced Micro Devices, Inc. (AMD), Google tensor processing units, Amazon Trainium, Microsoft Maia, or custom application-specific integrated circuits weaken the overall system. Competition increases supply, creates more price and power options, reduces dependence on one road map, and gives customers bargaining power.
The same logic applies to models. Closed-weight services spread infrastructure costs across large user bases and offer convenience and continuous improvement. Open-weight models provide control, privacy, sovereignty, customization, and independence from a model company, while shifting hardware, security, integration, and maintenance obligations to the user. Both can expand the market for compute, memory, cloud services, integration, and power.
This produces a counterintuitive result: the system can grow while profit migrates away from today’s apparent frontline leaders. Model prices can fall, chip competition can increase, and some AI companies can fail while demand for the complete stack continues to rise.
The existence of a large market does not identify its eventual profit pool.
The strongest bubble argument is partly right
The strongest objection to this thesis is that the system may be overbuilt. Companies can mistake experimentation for durable demand. Strategic investors can reinforce one another’s assumptions. Vendor financing can pull future purchases into the present. Rapidly improving hardware can shorten asset lives. Capital can chase scarcity just as new supply arrives.
All of that is plausible. It also fits the industrial-system thesis.
The late-1990s Internet boom produced fiber, data centers, software, and companies that helped build the modern economy. It also produced bankruptcies, unused capacity, weak business models, and terrible investments. The Internet succeeded; many Internet companies and investors did not.
The present buildout has important differences. Much of the capital comes from profitable companies with global customers, existing cloud operations, distribution, and the ability to use AI internally; we say that “they are eating their own dogfood.” The system also has substantial current revenue and production workloads. It still includes frontier laboratories, specialized cloud companies, projects, and financing structures with unproven long-term economics.
Compared to the Internet boom, there is less blind capital, not no blind capital.
This is why the public industrial thesis and a private investment thesis must remain separate. A system can become essential while competition compresses margins. A data center can be fully used while its financing produces a poor return. A transformative technology does not suspend price, underwriting, depreciation, or execution risk.
Governance must follow the whole system
Regulation aimed only at frontier models sees too little. AI-related outcomes also depend on semiconductor supply chains, cloud concentration, cybersecurity, power markets, grid reliability, water, construction, labor, insurance, finance, procurement, professional standards, and the organizations that deploy the systems.
Many of these areas already have governance. Utilities operate under reliability rules. Banks have capital and underwriting requirements. Public companies report financial information. Professional licensees remain responsible for their work. Data centers face building, environmental, electrical, permitting, and zoning rules. Semiconductor exports are regulated. The question is not whether AI should be governed. It is whether existing institutions identify the real mechanism and whether any gap justifies a new rule.
System-level analysis also keeps responsibility human. Software does not decide how much capital to allocate, where to build, which safety controls to require, who may use a system, or whether an output is good enough to act on. People and institutions make those decisions, even when responsibility is distributed across many of them.
That continuity links this paper back to the first four essays. AI remains software. Its outputs reflect a point of view. Describe its risks through mechanisms rather than stories about machine motives. Its value compounds only when people and organizations supply judgment, context, and discipline. The industrial system makes the capability possible; humans still decide what it is for.
What would change my mind?
A serious system thesis must identify evidence that would weaken it. I would materially revise this view if several of the following persisted across companies, architectures, and regions:
AI usage, useful tasks, and enterprise renewals declined even as capability improved and cost per task fell.
The largest cloud companies broadly canceled capital projects because customer demand had weakened, rather than because projects shifted location or timing.
Leading-edge fabrication and advanced packaging remained underused across several processor architectures.
Orders for high-bandwidth memory, networking, and power equipment fell together rather than moving among suppliers.
Developers surrendered power reservations and energized data-center capacity across regions because the computing had no economic use.
Customer revenue and cash flow repeatedly failed to support infrastructure debt and equity.
Supplier financing became the dominant source of purchases while independent utilization and collections deteriorated.
Smaller or local systems achieved comparable useful work with so little infrastructure that the relationship among capability, compute, and energy fundamentally changed.
Temporary delays, price declines, or the failure of individual companies would not be enough. The evidence would need to challenge demand, the ability to solve constraints, or the economics of useful work across the system.
The model to keep watching
Major technological shifts rarely appear from nowhere. The underlying ideas can be old while the convergence is new. Artificial intelligence combines decades of research with advanced semiconductors, global networks, cloud platforms, enormous pools of capital, and distribution to billions of people. Each element makes the others more useful. In many ways, AI is an old idea that has been waiting for industry to create an adequate infrastructure.
That does not make the system inevitable in every form. It makes it adaptive.
AI’s future will not be determined by a model benchmark or a single company’s road map. It will be negotiated across foundries, memory plants, data centers, utilities, capital markets, regulators, software systems, organizations, and users. The constraint will move. Profits will move with it. Some forecasts will fail, some projects will be stranded, and some participants will discover that an essential industry can still be a poor investment at the wrong price.
The larger system will keep trying to convert cheaper computation into more useful work. That is the model worth watching.
Questions, corrections, or disagreements are welcome. You can reach me directly at dave@aworkingmodel.com.