AI Is Getting More Expensive for You and More Expensive for Everyone Building It. Only One of Those Groups Is Celebrating.
The free tier is shrinking. The token bills are growing. And the companies losing billions on every query are currently valued at hundreds of billions of dollars. At least one of those things has to g
You’ve noticed this, even if you haven’t put a number to it. The things that used to be free in AI are becoming paid features. The messages you used to get in bulk now run out faster. Image generation that came included is now metered or gated behind higher tiers. Enterprise companies that enthusiastically pushed their employees toward AI tools are now looking at monthly invoices in the millions and having difficult conversations about whether any of it is worth it.
Wall Street is in a state of excitement about artificial intelligence that is genuinely hard to overstate. Startups are raising not millions but billions, sometimes hundreds of billions. Governments are treating AI sovereignty the way previous generations treated nuclear capability. The largest companies in the world are announcing capital expenditure plans that dwarf anything the technology industry has previously attempted.
AI becoming more expensive for users and AI investment accelerating are not mutually exclusive phenomena. The common root of these two symptoms is a structural problem, and comprehending it necessitates an examination of the operational costs of these systems.
The Teleportation Problem
Let me use an analogy that will become uncomfortable as soon as you recognize what it maps to.
Imagine someone invents teleportation. Not a simulation of teleportation, not a metaphor, but genuine instant physical transport. It works, and the demonstration is real. The implications are revolutionary. The problem is that teleporting one object 100 kilometers currently costs $1,000 in energy and infrastructure. It works better than every alternative; it’s just that it costs many times more than every alternative.
You can still build a business around it. No one would purchase the technology if it were priced at its actual cost. So you charge $150. And every time you move something, you absorb an $850 loss and hope that scale, efficiency, and time will eventually bring the cost down to a point where the gap closes.
Roughly, what running generative AI looks like today.
Running ChatGPT costs OpenAI an estimated $700,000 per day in compute and infrastructure. OpenAI’s audited 2025 financial documents, obtained by technology writer Ed Zitron and independently confirmed by the Financial Times, show the company recorded a net loss of $38.5 billion in 2025. Revenue was $13.07 billion, more than tripling from 2024. But total costs and expenses reached $34 billion. The net loss was amplified to $38.5 billion by a $41.55 billion charge related to the conversion from a nonprofit to a for-profit entity; the operating loss alone was $20.92 billion. Either figure is historically unusual for a company receiving this level of investor confidence.
The standard response to this, the one you’ve probably heard, is that this is normal for transformative technology in its early phase. New technologies are always expensive at first. Costs come down with volume and experience. The same economics that made computing affordable eventually apply to everything.
Here’s the problem with that argument applied to generative AI: the curve is going in the wrong direction. Since the launch of ChatGPT approximately four years ago, running the systems has not gotten cheaper in aggregate. It has gotten more expensive. Every capability improvement requires more compute. Every performance improvement consumes more energy. The race between labs to maintain a frontier position means that the savings from efficiency are immediately consumed by the push to the next capability level. The cost reduction that the argument promises keeps getting deferred by the ambition the market rewards.
The Business Model We Haven’t Solved Yet
The hyperscalers, meaning Google, Microsoft, Amazon, and their peers, have announced aggregate AI-related capital expenditure that exceeds one trillion dollars across 2025 and 2026. These are genuine commitments, already being turned into physical infrastructure: data centers, power agreements, custom chips, network buildouts.
The revenue coming back against those expenditures, across the AI industry, currently represents roughly 15 cents on the dollar spent. The gap isn’t closing. It’s widening because competitive pressure forces companies to deploy capacity before demand justifies it.
This is not a temporary growth-stage challenge with a clear resolution path. It’s a structural mismatch between what it costs to produce AI output and what the market will pay for it. For individual companies, the business logic is to keep investing because the alternative, falling behind in the capability race, is assumed to be worse than the losses. For the industry, no single company’s decision to keep investing changes the underlying arithmetic of what the infrastructure costs and what the services generate.
OpenAI’s relationship with Microsoft makes this concrete in a way that should concern anyone watching closely. In 2025, OpenAI paid Microsoft $17.2 billion for research and development, compute services, and related expenses. Microsoft has been one of OpenAI’s largest investors, providing capital that OpenAI then uses to build on Microsoft’s infrastructure. In the same year, Microsoft paid OpenAI $303 million. This is a documented circular financial relationship: investment flows in, and a large portion flows back out to the investor as service payments, creating revenue for the investor without requiring external customers.
This practice, sometimes called round-tripping in financial analysis, is not unique to this pair. Nvidia, which has arguably benefited more than any company from the AI investment cycle, also invests in AI startups. Those startups use that capital to purchase Nvidia’s chips. Nvidia’s revenue grows while the stock rises.
Nvidia’s ability to invest in more startups increases. The FTC and several other regulatory bodies are reported to be examining these investment structures for exactly this reason. An unwinding of those relationships, or a regulatory intervention that forces cleaner separation between them, could be the trigger that breaks the cycle.
