The largest AI companies already have models capable of solving tasks that seemed almost like science fiction a few years ago. But this does not mean that every user of a free mass-market service gets precisely that level of intelligence.

The reason is quite prosaic: high-quality machine reasoning costs money and computational resources.

If one complex query requires a bit more computation, the difference is almost unnoticeable for a single person. If that same service serves hundreds of millions or a billion users, a slight increase in the cost of each response turns into massive expenditures on electricity, hardware, data centers, and infrastructure maintenance.

This is the same principle that applies in many technologies. You can build a very expensive car capable of traveling faster and safer than a mass-market one. But you cannot automatically make it a standard free car for billions of people. Economics always emerges between the technological maximum and the mass product.

With AI, this gap is especially important because the user does not always see it. On the screen, everything looks the same: a question field and a text response. But one query might be processed by a stronger system with a larger volume of computations and multiple verification stages, while another is handled in a cheaper and faster way.

Google demonstrates the scale of this limitation well today. In May 2026, the company reported that AI Mode exceeded a billion monthly users in just one year, and the number of queries continued to grow rapidly. Google on the scale of AI Mode. At the same time, in 2026 Alphabet sharply increased capital expenditures on AI infrastructure; Reuters reported planned spending of around $195–205 billion. Reuters.

These figures are important not as proof that Google's specific response is worse than another AI's. They illustrate the very physics of a mass-market service: even a company of Google's scale is forced to calculate the cost of intelligence per query.

This gives rise to a paradox. A company may have a stronger model, but the free mass-market product gets a cheaper configuration. This does not mean that the company "doesn't know how to do better." On the contrary, the question arises precisely because it does know how to do better: why isn't this best level available to everyone all the time?

The answer is almost inevitably tied to a combination of cost, speed, and available capacity. The longer the system thinks, the more information it checks, and the more heavily it utilizes the model, the more expensive the maintenance becomes. The provider has to choose what level of quality can economically be given to everyone, and what to leave for expensive pricing tiers, complex modes, or a limited number of requests.

Therefore, technological capability does not yet mean economic viability. This is one of those cases where it is not enough for a futurist to look at the best model demonstration. One needs to look at how much it costs to turn that demonstration into an everyday service for billions of people.

Several important things follow from this for the end user.

First, free mass-market AI does not necessarily show the upper limit of a company's capabilities. If you tried a free service and got a mediocre result, it doesn't always mean that the manufacturer's entire technological lineup is equally weak.

Second, paid subscription tiers increasingly sell access to a greater volume of intellectual work rather than just cosmetic convenience. This is already visible at OpenAI and Anthropic: more expensive plans provide expanded use of strong modes, longer reasoning, and additional models.

Third, as AI services grow, a new stratification will emerge: people will formally have identical access to the internet, but differing access to the quality of its machine interpretation. This is a separate topic in itself — “Information quality becomes an access level”.

The conclusion is also important for business. If a company builds a critical process around external AI, it cannot operate on the assumption that the maximum level of quality will always be available at today's price. Intelligence is becoming a variable cost. As load increases, the provider may change tariffs, limits, and operating modes, and the product's own economics must be able to withstand this.

The main idea is simple: mass-market AI is limited not only by what humanity knows how to build, but also by how much intelligence humanity can afford to continuously maintain at scale. Therefore, the future of AI is determined simultaneously by the progress of models and the progress of computing infrastructure, energy, and the economics of their operation.