AI is making intelligence dramatically cheaper. It is also turning intelligence into a metered input. That changes software margins, pricing, power grids, capital markets, and ultimately which countries and companies capture the economics of AI.
There is a strange contradiction sitting at the center of the AI economy.
For most of the software era, the dream was to make the marginal cost of serving one more customer disappear. Build the product once. Put it in the cloud. Add another user. The extra cost existed, obviously, but compared with the subscription revenue it was often tiny.
AI breaks that mental model.
Every time an AI system reads a document, reasons through a problem, generates code, searches a database, calls another tool, checks its own work, or runs another loop inside an agent, somebody has to pay for computation.
That payment might be a fraction of a cent. It might eventually fall another hundredfold. It may be hidden inside a subscription. But it exists.
And as AI moves from answering questions to executing transactions, that variable cost gets embedded deeper into the economy. OpenAI’s latest enterprise data show exactly that migration: enterprise usage is moving from assistance toward execution, and the firms in the top decile of AI usage now generate 8.3 times as many output tokens per active user as typical firms. As of June 2026, Codex alone produced 64% of the combined Codex and ChatGPT output tokens among OpenAI enterprise customers. Usage of enterprise Codex since February grew 108-fold in legal, 41-fold in sales, 41-fold in recruiting, and 26-fold in marketing.
That is the beginning of what I think of as the compute tax.
Not a tax imposed by government. And not literally a GPU charge on every payment made in the economy.
It is a new production cost attached to every economic action that requires machine intelligence.
The distinction matters because AI is simultaneously doing two things that look contradictory.
It is destroying transaction costs by making search, analysis, communication, negotiation and execution cheaper. MIT researchers describe the fundamental economic promise of AI agents in almost exactly those terms: agents can substantially reduce the time and effort involved in searching, communicating and contracting. But the mechanism doing that work consumes compute.
So AI may eliminate a $20 human coordination cost and replace it with 20 cents of machine inference.
That’s an extraordinary economic bargain.
But the 20 cents still has to go somewhere.
And once trillions of decisions, searches, negotiations, customer interactions, software changes, purchases and administrative actions become mediated by AI agents, those pennies start looking less like a rounding error and more like an entirely new layer of the economy.
The tax hiding inside the product
The easiest way to understand the change is to stop thinking about tokens as a technical metric.
A token is not a business outcome.
Nobody cares that an AI consumed 80,000 tokens to resolve an insurance claim. The insurer cares whether the claim was resolved correctly, how long it took, how many humans had to intervene, how much the computation cost, and whether the final cost was lower than the old process.
OpenAI is now explicitly telling CFOs to think this way. Its proposed economic scorecard asks whether the value of work completed grows faster than the cost required to produce it. It argues that cost per token can be misleading because a cheaper model may require retries, longer processing or human review, while a more capable and more expensive model may complete the task correctly in one pass.
BCG has arrived at almost the same conclusion independently. It argues that the useful denominator is cost per successful outcome, because agentic workloads can accumulate cost through long context windows, repeated loops, retrieval, tool use and model selection. A weaker model can therefore cost more in total than a stronger one if it repeatedly fails before reaching an acceptable answer.
This sounds like a minor accounting change.
It isn’t.
It means intelligence is becoming something companies can buy in variable quantities.
Consider a deliberately simple example using OpenAI’s published API pricing in August 2026. GPT-5.6 Terra is currently priced at $2 per million input tokens and $12 per million output tokens for standard processing below the stated context threshold. A workload using 20,000 input tokens and producing 5,000 output tokens therefore costs roughly 10 cents for that individual model invocation: four cents of input and six cents of output. One million identical calls would cost roughly $100,000. A workflow requiring ten such calls would be around $1 of model inference per completed workflow before counting external tools, searches, retries, storage, conventional cloud infrastructure or human review.
That is not an estimate of what an average agent costs. Agent economics vary enormously.
It is simply a way of seeing the scale.
A dollar of machine intelligence can be insanely cheap if it replaces thirty minutes of professional labor.
A dollar can also be catastrophically expensive if the product earns fifty cents from the interaction.
That difference is going to separate a lot of good AI businesses from bad ones.
First-order economics: software gets a meter
The first-order consequence is the easiest one.
AI turns part of software back into a variable-cost business.
BCG makes the accounting point bluntly: tokens used inside products and customer interactions belong in cost of goods sold. Unlike conventional SaaS economics, where incremental software usage could often be served at very low marginal cost relative to revenue, inference rises as customers interact with the model. BCG’s current analysis estimates gross margins of roughly 65% to 80% for AI-enabled software and 50% to 65% for AI-native products, though these are estimates of emerging economics rather than a universal law for every AI company.
That creates a surprisingly old-fashioned problem for supposedly futuristic companies:
What does it cost to produce one unit of what you sell?
The SaaS industry spent twenty years training investors to care about seats, annual recurring revenue, retention and sales efficiency. AI forces management teams to relearn manufacturing economics.
Except the factory is a data center.
For a customer-service company, the unit might be a resolved ticket.
For an AI coding company, it might be a successfully merged code change.
For a legal agent, a reviewed contract.
For a procurement system, a completed sourcing decision.
For an AI-native bank, perhaps a completed underwriting decision.
The important number is no longer merely:
Revenue per user.
It starts becoming:
Revenue per successful outcome minus compute per successful outcome.
And the word successful is doing a lot of work there.
Suppose Model A costs 5 cents and completes the task correctly 60% of the time, while Model B costs 15 cents and succeeds 95% of the time. Once you account for retries, escalation and human review, Model B can easily have better economics despite charging three times more per inference pass. Both BCG and OpenAI now emphasize this distinction between nominal model price and full cost of successful work.
This is why there will not be one compute tax.
There will be millions of them.
Different workflows will have radically different intelligence intensity.
Generating an email subject line and autonomously negotiating a commercial contract may both appear as a single click to the user. Economically, they are completely different products. Agentic systems may retrieve documents, maintain long contexts, reason repeatedly, use external tools and inspect intermediate results before producing a final action. BCG notes that these loops can make apparently similar requests carry very different costs, while OpenAI’s enterprise data show the leading users consuming far more output tokens as they move toward delegated, multistep work.
That creates an immediate strategic consequence.
Model architecture becomes business-model architecture.
Routing a trivial request into an expensive frontier model is no longer just sloppy engineering. It is margin leakage.
Using deterministic software for something an agent unnecessarily reasons about is margin leakage.
Repeatedly feeding gigantic context windows into a model is margin leakage.
Failing to cache reusable information is margin leakage.
Letting an agent loop without an economically rational stopping condition is margin leakage.
The companies that understand this are already moving toward routing, caching, batching and task-specific model selection rather than sending every problem to the biggest available model. BCG explicitly recommends separating deterministic tasks from model-based reasoning and routing simpler work toward lighter models while reserving expensive models for difficult tasks.
In other words, the AI stack is starting to develop something industrial companies have understood forever:
cost engineering.
And that is probably healthy.
Because the real question was never whether AI was expensive.
The question is whether the intelligence purchased for $1 creates $1.10 of value or $100 of value.
Second-order economics: the business model starts mutating
The second-order effect is more interesting because companies cannot simply absorb a new variable cost forever.
Eventually pricing has to adapt.
This is already visible across software. McKinsey argues that inference is introducing recurring compute and infrastructure costs into software economics and that vendors increasingly need pricing structures that scale with usage, outcomes, actions or compute rather than relying purely on traditional seats.
That is why the seat may gradually lose its status as the dominant unit of enterprise software.
A seat makes sense when a human is the worker.
What is a seat worth when one employee launches 500 autonomous tasks overnight?
What happens when a company has 3,000 employees but 40,000 persistent software agents?
Charging $30 per employee starts looking ridiculous if the agent produces $10,000 of work. Equally, an “unlimited AI” subscription starts looking suicidal if one power user can unleash millions of tokens of autonomous work.
So AI pricing is likely to migrate toward something closer to economics itself:
work performed.
Per resolution.
Per transaction.
Per code change.
Per lead qualified.
Per invoice reconciled.
Per claim processed.
Per successful research task.
That does two things at once.
First, it makes AI companies more directly exposed to their customers’ economics. If the AI cannot produce a valuable outcome, it becomes difficult to keep charging merely because someone has a login.
Second, it creates a powerful incentive for AI companies to drive the compute required per successful outcome downward.
That could be a much stronger productivity engine than merely making models cheaper.
BCG points to emerging AI companies already charging around outcomes rather than conventional seats and argues more broadly that AI is reallocating profit pools toward businesses that control valuable customer relationships, proprietary assets and difficult-to-replicate workflows. Its August 2026 work describes AI as a reallocation of capital and margins rather than merely another productivity technology.
This is where the compute tax starts changing competitive strategy.
Imagine two AI legal companies.
Both use roughly the same frontier models.
Company A is basically a wrapper. It buys expensive inference, adds an interface and resells it.
Company B has proprietary legal data, a retrieval layer, specialized evaluation systems, model routing, workflow integration, cheaper models for routine work, frontier models for hard questions and a feedback system that improves as customers use it.
The second company may pay the same headline API price as the first.
Its cost per successful legal outcome can still be dramatically lower.
That means the durable moat in AI software may not be “we have AI.”
Almost everybody will have AI.
The moat becomes the system surrounding the model that converts compute into useful work efficiently.
Data.
Workflow.
Distribution.
Routing.
Evaluation.
Customer context.
Proprietary feedback.
Trust.
And critically, the ability to determine when not to use expensive intelligence.
That last point is underrated.
The cheapest AI call is the one you did not need to make.
This also explains a seeming contradiction in the economics of AI agents.
MIT researchers argue that agents can dramatically reduce traditional transaction costs by lowering the effort involved in searching, communicating, comparing alternatives and contracting. They describe agents as potentially useful even in complex markets such as procurement, investment and real estate because software can inspect far more information than a human economically could.
I think they’re right.
But the transaction cost isn’t disappearing.
It is being converted.
We are swapping human transaction costs for machine transaction costs.
Instead of paying an employee to spend three hours comparing 70 suppliers, you might pay an agent to interrogate 70 databases, read 1,000 documents, request quotations, score the offers and return three candidates.
Human coordination cost collapses.
Compute consumption rises.
The net economic result can be overwhelmingly positive.
This is why calling compute a tax should not be confused with calling it a burden.
A good tax can still be a bargain when the alternative is much more expensive.
The strategic issue is who captures the savings.
Suppose an old process costs $100 in human labor.
AI reduces total production cost to $10, of which $2 is inference.
There is now $90 of economic surplus available.
Does the customer keep it through lower prices?
Does the AI application capture it?
Does the model provider capture it?
Does the cloud provider?
Does the GPU supplier?
Does the owner of proprietary data?
Does competition push almost all $90 back to the consumer?
That is the real capital-allocation question.
BCG’s latest work argues that AI is shifting profit pools and that AI-native attackers may selectively capture high-margin parts of incumbent value chains without reproducing the incumbent’s entire business.
Which means AI may not simply make existing businesses more efficient.
It may unbundle their margins.
Third-order economics: the token bill reaches the physical economy
Now zoom out another level.
A token looks digital.
Its supply chain isn’t.
Every token ultimately descends through a physical stack:
model software,
servers,
accelerators,
high-bandwidth memory,
networking,
data-center buildings,
cooling,
transformers,
transmission,
electricity generation,
land,
water,
capital.
Once enough intelligence is consumed, the compute tax becomes an infrastructure bill.
The International Energy Agency says global data-center electricity consumption reached roughly 485 TWh in 2025 and projects around 950 TWh by 2030, roughly 3% of global electricity demand. Electricity consumption specifically associated with AI-focused data centers is expected to triple over the same period.
And here is the really important part.
The electricity required per AI task is falling rapidly.
Yet total electricity consumption is rising.
The IEA says per-task efficiency is improving at a rate it describes as unprecedented in energy history, while at the same time more people are using AI and more compute-intensive applications such as agents are spreading. Its current forecast therefore still has data-center electricity consumption doubling by 2030.
That is the entire compute-tax thesis in miniature.
The tax rate falls.
The tax base explodes.
We have seen versions of this before. Make something dramatically cheaper and people don’t necessarily spend less on it. They find more things to do with it.
Cheap bandwidth didn’t cause the world to consume less bandwidth.
Cheap storage didn’t cause us to store less data.
Cheaper computation didn’t cause us to compute less.
Cheap machine intelligence could follow the same pattern.
The biggest economic question is therefore not:
How cheap will one token become?
It is:
How much machine intelligence will the economy demand once intelligence becomes cheap?
That question reaches directly into electricity markets.
The U.S. Department of Energy’s latest Lawrence Berkeley National Laboratory modeling estimates that data centers could reach 11.8% of total U.S. electricity consumption by 2030, with scenarios ranging from 9.5% to 15.3%.
The IEA estimates that more than 40% of the additional electricity required by global data centers through 2030 will still be met by natural gas and coal, even as renewable generation grows rapidly. The mix varies sharply by geography: U.S. data centers currently rely most heavily on natural gas, while Chinese data centers remain much more coal-intensive; Europe is projected to move toward a data-center electricity mix dominated by renewables and nuclear.
Then comes the capital bill.
The IEA says the capital expenditure of five major technology companies exceeded $400 billion in 2025 and is projected to rise a further 75% in 2026. It also warns that data-center expansion has become too capital intensive to rely only on corporate balance sheets, making future construction increasingly sensitive to capital markets, financing conditions and investor expectations about AI returns.
Federal Reserve Governor Lisa Cook pointed to more than $1.5 trillion of announced data-center plans, only a small fraction of which had been realized as of May 2026. She also noted rising prices in chips, high-tech equipment and software and stronger wage pressure in specialized construction trades as the investment boom feeds through the supply chain.
That is third-order economics.
The first-order question was:
How much does this AI answer cost?
The second-order question was:
What happens to the company’s margins and pricing model?
The third-order question is:
What happens when the entire economy tries to buy the chips, transformers, power plants, construction workers and financing required to generate trillions of those answers?
Now the economic consequences escape the technology sector.
A shortage of transformers becomes an AI constraint.
A gas-turbine backlog becomes an AI constraint.
A grid interconnection queue becomes an AI constraint.
A transmission project becomes AI infrastructure.
The IEA says shortages and delays now span transformers, gas turbines, advanced chips and high-bandwidth memory, with planning and grid-connection systems also under pressure.
And once compute begins competing for scarce physical capacity, the tax can spill over onto people who never asked for the AI product in the first place.
That is exactly why U.S. policy has started focusing on tax incidence, even if Washington doesn’t use that phrase.
In March 2026, the White House announced a Ratepayer Protection Pledge asking major hyperscalers and AI companies to “build, bring, or buy” the energy needed for new data centers, pay the cost of required power-delivery upgrades and negotiate structures under which they still pay for capacity created for their facilities even when it is unused. The explicit rationale is to prevent ordinary electricity customers from absorbing infrastructure costs created by data-center demand.
That is worth pausing on.
The government is effectively saying:
The compute tax should stay inside the AI economy rather than leaking onto everybody else’s electricity bill.
That is not really an AI regulation.
It is industrial economics.
And every country is approaching it differently.
Four economies, four ways to pay the bill
The competition between the United States, China, Europe and India is usually described as a race for models.
I think that’s becoming too narrow.
It’s increasingly a contest over who builds the cheapest, deepest and most strategically controlled supply of machine intelligence.
The Federal Reserve’s comparative work identifies compute as one of the clearest measures of national AI capacity. Based on observed high-end AI supercomputer capacity through mid-2025, it estimated roughly 74% in the United States, 14% in China and 4.8% in the EU, while cautioning that the underlying data capture only part of global capacity and that Chinese data are comparatively opaque.
But the countries are not simply building different quantities of the same thing.
They are building different political economies around compute.
The United States is treating compute primarily as a private-capital industry.
The U.S. advantage is extraordinary depth in private technology capital, hyperscalers, model companies, data centers, chips and cloud infrastructure. The policy response has largely tried to accelerate physical buildout while increasingly addressing the externalities that buildout creates. The White House’s 2026 ratepayer framework is a good example: let private companies build aggressively, but push the incremental grid and generation costs back toward the companies creating the demand.
That’s basically a market-led model with industrial-policy support.
Capital allocators decide where to build.
Technology companies decide which architectures win.
Energy markets respond.
Government increasingly tries to remove permitting and infrastructure constraints while preventing some of the cost from being socialized.
The strength of this model is brutally obvious: scale and capital.
The weakness is also obvious: private firms will optimize around private returns, which can collide with grid planning, local politics, electricity affordability and infrastructure lead times. The IEA now identifies U.S. grid queues, power equipment and generation as practical bottlenecks to the pace of data-center construction.
China is thinking about compute more like national infrastructure.
The distinction is striking.
China’s National Development and Reform Commission describes the country’s emerging national compute network as infrastructure that links computing resources across regions. The “East Data, West Computing” program was designed partly to shift compute demand from economically dense eastern regions toward western regions where energy resources are more abundant. Eight national computing hubs and ten data-center clusters formed the initial national structure, and subsequent policy has moved toward integrated scheduling and more market-oriented allocation of compute resources.
China’s current Five-Year Plan framework goes further, calling for an integrated national computing network with resource pooling, monitoring, scheduling and operating standards while coordinating compute-network development with the electricity grid and communications infrastructure.
That is a very different way of conceptualizing the problem.
America largely asks:
Where will private capital build compute?
China increasingly asks:
How should the national compute system be architected?
That doesn’t mean China has eliminated markets. Its policy language explicitly discusses marketization and resource allocation. But the physical architecture is much more deliberately coordinated at the state level.
China also has a potentially enormous energy advantage. Federal Reserve research notes that China’s power-generation infrastructure expanded far faster than that of the United States in recent years, even though the U.S. currently retains a commanding lead in high-end compute capacity.
There is a catch.
China’s current data-center electricity supply remains much more carbon intensive. The IEA estimates that coal supplies close to 70% of the electricity serving Chinese data centers today, with renewables near 20% and nuclear around 10%. Policy is deliberately steering more facilities toward renewables-rich western regions, and both coal and renewable generation are projected to expand materially in response to data-center demand through 2030.
So China is doing something strategically clever but environmentally messy:
It is treating the geography of compute and the geography of energy as the same problem.
That idea may turn out to matter enormously.
Europe is trying to socialize part of the fixed cost of strategic compute.
The European Union’s model sits somewhere else again.
Rather than relying exclusively on a handful of domestic hyperscalers that Europe largely does not have, the EU is building shared AI infrastructure through AI Factories and the newer AI Gigafactory program.
In July 2026, the EU launched a tender for as many as seven AI Gigafactories. The initiative is backed by up to €10 billion in EU and national public funding and is designed to unlock at least €20 billion more from private investors. The planned facilities combine processors, cloud software, connectivity and energy-efficient data centers and are meant to provide compute access to startups, scale-ups, industry, researchers and public authorities.
Europe is therefore making a different bet:
Compute capacity itself has become strategic infrastructure, and some of its fixed cost should be pooled because dependence on foreign compute can become an industrial dependency.
The EU explicitly frames these facilities around technological resilience and strategic autonomy as well as compliance with European rules on safety, data protection and security.
Whether that produces globally competitive AI companies is not guaranteed.
But economically, the logic is clear.
Europe is trying to lower the compute tax faced by domestic innovators by helping finance the fixed infrastructure underneath them.
India is doing something more direct: subsidizing access.
India’s model may be the most explicit acknowledgment that compute access itself is an economic input.
The government’s IndiaAI Mission has a budget of roughly ₹10,372 crore and, by June 2026, had expanded a shared national compute pool to more than 45,000 GPUs. By August, according to the government, 237 projects had used subsidized computing capacity covering 9.318 million GPU hours, specifically to reduce barriers for researchers, startups and innovators.
India cannot currently outspend the American hyperscalers.
So instead it is trying to change the economics faced by the user.
Rather than every startup individually acquiring scarce compute at global market prices, government procurement aggregates capacity and makes it cheaper to access.
My read is that these four models amount to four different answers to the same question:
Who should absorb the fixed cost of making machine intelligence abundant?
America: mostly private capital.
China: coordinated national infrastructure plus markets.
Europe: public-private sovereign infrastructure.
India: subsidized shared access.
That distinction could become as consequential as differences in model capability.
Because in an agentic economy, the country with the cheapest reliable intelligence input may eventually have an advantage similar to the country that once had cheap electricity, cheap labor or cheap capital.
The argument experts are actually having
There are at least three big disagreements here, and they are more interesting than the usual “AI good versus AI bad” debate.
The first is whether the compute tax survives at all.
There is a strong case that inference becomes so cheap that worrying about it is pointless.
The evidence for this view is formidable. Stanford’s AI Index found that the cost of running a model at roughly GPT-3.5-level benchmark performance dropped from about $20 per million tokens in November 2022 to $0.07 by October 2024, a decline of more than 280-fold in roughly 18 months. Across different tasks, Stanford reported inference-price improvements spanning roughly 9-fold to 900-fold annually. McKinsey has highlighted the same underlying cost collapse as one of the defining AI technology trends.
That trend is real.
The mistake is concluding that falling unit costs make aggregate compute economics irrelevant.
The IEA is observing the opposite phenomenon in electricity: energy per task is dropping rapidly while total AI electricity demand accelerates, because adoption and task intensity are growing faster.
Those two things can coexist indefinitely.
Imagine inference becomes ten times cheaper.
Then imagine agents become cheap enough that businesses run one hundred times as many autonomous tasks.
The unit tax falls 90%.
The aggregate compute bill rises tenfold.
That is why I don’t think the right debate is:
Will inference get cheaper?
Almost certainly.
The better debate is:
Will efficiency improve faster than demand for machine intelligence expands?
Right now, nobody can answer that confidently.
The second disagreement is whether all this compute produces an economic return commensurate with the capital going into it.
Here the range of credible expert opinion is enormous.
McKinsey’s latest work on agents and robotics estimates that a midpoint adoption scenario could unlock roughly $2.9 trillion annually in U.S. economic value by 2030, primarily through automation and the redeployment of labor hours. McKinsey is careful to call this potential economic value rather than a forecast of equivalent GDP growth and says realizing it depends heavily on workflow redesign and organizational adaptation.
Daron Acemoglu at MIT is much more skeptical about macroeconomic impact. His modeling places the ten-year increase in total factor productivity from current AI advances at no more than around 0.66%, with an estimate around 0.53% once harder-to-learn tasks are treated more conservatively. His corresponding GDP estimates are roughly 0.93% to 1.16% over ten years under a modest investment response and as high as roughly 1.4% to 1.56% under a larger capital boom.
These estimates are not directly comparable. McKinsey is measuring the potential economic value of work that could be automated or reallocated under an adoption scenario; Acemoglu is estimating economy-wide productivity and GDP effects using a much more restrictive macro framework. But the gap tells you just how unsettled the economics still are.
The Federal Reserve sits somewhere in the middle.
Its July 2026 analysis says task-level experiments can show substantial improvements without those improvements immediately appearing at the company or national level. Making a programmer 10% faster at one task does not automatically make the company 10% more productive if bottlenecks elsewhere remain. The Fed says there was still no large aggregate productivity signal clearly attributable to AI as of 2026, while emphasizing that historically, general-purpose technologies often require years of complementary investment and organizational change before productivity gains become visible.
The OECD reaches a similar conclusion: controlled and workplace studies often report 20% to 40% performance improvements on specific tasks, depending on context, while the economy-wide and long-run consequences remain uncertain.
This disagreement matters enormously for investors.
Because a trillion-dollar compute buildout is very easy to justify if AI ultimately reorganizes several trillion dollars of annual labor and economic activity.
It is much harder to justify if most companies end up buying expensive copilots that make employees moderately faster without fundamentally changing output.
That is why the decisive variable isn’t benchmark intelligence anymore.
It is organizational conversion.
Can companies turn model capability into completed economic work?
OpenAI, McKinsey, BCG and the Federal Reserve, despite coming from very different positions, are converging on that point: deeper workflow redesign and successful outcomes matter more than raw usage.
The third disagreement is subtler.
Does AI raise transaction costs or destroy them?
My answer is:
Both.
AI agents are likely to crush a huge category of Coasean transaction costs: searching for information, finding counterparties, comparing options, negotiating terms, monitoring performance and coordinating activity. MIT researchers explicitly identify this reduction in search, communication and contracting friction as one of the fundamental economic promises of agents.
But they do it by substituting computation for human effort.
That means an old transaction that might have cost:
$50 of employee time
$20 of administration
three days of latency
could become:
$0.70 of compute
$0.10 of database and search access
thirty seconds
The compute tax rose from zero to 70 cents.
The total economic transaction cost collapsed from $70 to less than a dollar.
There is no contradiction.
And this distinction is critical, because it tells us not to optimize for minimum compute spending.
The objective should be minimum total cost per valuable outcome.
OpenAI calls its version “Useful Intelligence per Dollar.” BCG calls for measuring return against the combined costs of human intelligence and tokens. Both frameworks point in the same direction.
Spend more compute when another dollar of inference replaces twenty dollars of labor.
Spend less compute when a deterministic API can solve the problem for a fraction of the cost.
Use an expensive frontier model when reliability saves five retries.
Use a tiny model when the task is trivial.
Use no model at all when normal software works.
This sounds obvious.
It isn’t how much of the industry has been built so far.
What the capital allocator should take away
I think the AI economy is heading toward a realization.
Intelligence is becoming cheap, but it is not becoming free.
And once intelligence becomes embedded in products, the economics of intelligence start appearing everywhere.
In gross margins.
In pricing models.
In cloud bills.
In electricity markets.
In data-center financing.
In transformer factories.
In grid regulation.
In government industrial strategy.
In national competitiveness.
The winners could be the companies that turn each dollar of computation into more economically valuable work than everybody else.
For founders, that means one of the most important metrics in the company may eventually be something deceptively simple:
Compute cost per successful customer outcome.
Not token consumption.
Not model calls.
Not users chatting with the agent.
Actual finished work.
For CEOs, it means AI expenditure should stop being buried inside a generic technology budget. BCG’s current framework separates AI expenditure among investment in reusable capabilities, operating costs for internal AI work, and COGS when inference sits inside customer-facing products. That distinction becomes increasingly important as agent use expands.
For VCs, I would be increasingly suspicious of AI businesses whose economics are basically:
buy intelligence retail, resell intelligence with a markup.
That is not automatically a moat.
The more interesting companies are the ones that can structurally change the ratio between compute consumed and value produced through proprietary data, workflow integration, model routing, specialized evaluation, distribution or ownership of the customer relationship. BCG’s latest competitive analysis similarly argues that durable advantage is shifting toward assets and relationships that improve with scale and use rather than generic capabilities that AI can easily replicate.
For infrastructure investors, the compute tax moves upstream.
Inference demand becomes server demand.
Server demand becomes electricity demand.
Electricity demand becomes generation, storage and transmission demand.
Those requirements create demand for chips, networking, memory, cooling, transformers, turbines, batteries and capital. The IEA already sees pressure and bottlenecks across many of these categories and expects capital markets to become increasingly important in financing continued data-center expansion.
For policymakers, the problem is even bigger.
The central policy question will increasingly be not merely how much AI a country develops, but what domestic businesses pay to access machine intelligence and who ultimately absorbs the infrastructure costs required to produce it.
America is trying to make private companies internalize more of the power-system costs their data centers create.
China is building a nationally coordinated compute network around the geography of electricity and demand.
Europe is using public capital to build shared sovereign compute capacity and pull in private investment.
India is directly subsidizing compute access for domestic innovators.
Those are not just AI policies.
They are different theories of who should pay for intelligence infrastructure.
And over the next decade, that may turn out to matter far more than most arguments about which chatbot currently wins which benchmark.
Because the important economic transformation isn’t that AI can answer questions.
It is that AI is beginning to do work.
Once an agent searches, reasons, compares, negotiates, codes, buys, sells, monitors and acts, computation stops being something that happens somewhere in the background of the technology industry.
It becomes an input into economic production.
Every AI-mediated action carries some amount of it.
The price of that input will keep falling. Stanford’s data already show how violently inference costs can collapse, while the IEA simultaneously shows how rapidly aggregate demand can rise as efficiency improves.
That is why the central paradox of the next phase of AI is so important:
The compute tax could approach zero per transaction while becoming enormous in aggregate.
That isn’t a bug in the AI economy.
It may be the AI economy.
We spent the software era turning computation into something users barely had to think about.
Now we are turning intelligence itself into a metered commodity.
And when intelligence has a price, the people who control its production, distribution, efficiency and energy supply don’t merely own pieces of the technology stack.
They sit inside the cost structure of everything built on top of it.