Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:
Does AI create more value than it costs?
My position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.
We are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.
And that gap could become one of the defining economic problems of the AI era.
We are building factories for intelligence
AI is usually described as software. Increasingly, that description is misleading.
Behind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.
AI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.
This changes the economics of computing.
Intelligence now has a cost of production.
And unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.
That is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.
The AI infrastructure paradox
This leads to what I call the AI infrastructure paradox.
The world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.
We can measure GPU utilization.
We can count tokens.
We can measure power consumption.
We can count AI users, agents, models and applications.
We can calculate cloud costs almost to the penny.
But ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.
That is a serious problem.
A company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.
We are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.
Someone ultimately has to pay
There is another side of the AI boom that receives far less attention: affordability.
Data centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.
Those costs cannot simply disappear.
Ultimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.
This is where AI encounters an economic constraint that no increase in model intelligence can eliminate.
If the cost of intelligence grows faster than the value of intelligence, eventually something has to give.
This also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.
Individual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.
I call this phenomenon IT Inflation.
Technology can simultaneously become more efficient and more expensive.
The important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.
The community eventually gets a vote
The physical scale of AI creates another economic problem.
The costs and benefits of AI infrastructure frequently occur in different places.
A community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.
But much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.
That creates a reasonable local question:
If we absorb the costs, where is our share of the value?
As AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.
The great measurement failure
This brings us to what may be AI’s biggest weakness.
It isn’t intelligence.
It is measurement.
Traditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.
But technology has evolved from a supporting function into something closer to a factor of production.
Like labor, technology performs work.
Like capital, it requires investment.
Like infrastructure, it enables economic activity.
AI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.
Yet our accounting and economic measurement systems have not caught up.
That is why I believe we need a discipline of technology economics.
From technology management to technology economics
Technology economics asks a different set of questions from traditional IT management.
Not simply: Does the system work?
But: What economic value does it create?
Not simply: How much AI are we using?
But: How productive is that AI?
Not simply: How much are we investing?
But: What return are we receiving relative to the capital consumed?
The framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.
Consider just two of these.
AI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?
AI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?
Similarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.
Two companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.
AI needs its own measurement revolution
Every major economic transformation eventually produced new ways of measuring itself.
Industrialization gave us modern accounting.
Mass production drove the development of productivity measurement.
Global finance produced increasingly sophisticated theories of capital, risk and return.
The Intelligence Economy will need its own measurement revolution.
Companies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.
Investors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.
Technology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.
The same problem exists at the national level.
GDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.
That suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.
Whether that particular measure ultimately becomes standard is less important than the underlying idea.
If intelligence has become an economic resource, eventually we will have to measure it as one.
The question that comes after the hype
None of this argues that AI will fail.
Quite the opposite.
AI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.
But technological capability does not repeal economics.
The railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.
AI may follow a similar pattern.
The critical divide may therefore not be between companies that adopt AI and companies that don’t.
It may be between those that understand the economics of intelligence and those that simply consume more of it.
We already know how to make machines increasingly intelligent.
The next challenge is learning how to make that intelligence economically productive, affordable and sustainable.
Communities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.
All of those demands eventually converge on one question:
What is intelligence worth?
The industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.
And the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.
It may be the measurement system that finally allows us to answer the question behind all of them:
Does the value created by intelligence exceed the cost required to produce it?
Everything else follows from the answer.