SENSE: why the LLM market is shifting models from service to paid access to thinking

The SENSE article suggests viewing large language models not as another digital service, but as new infrastructure. By this logic, users and companies will increasingly pay not for a separate application, but for access to "machine thinking" with clear pricing, quality, and control level.

LLM as Infrastructure

The author proposes stripping away the marketing wrapper and viewing the market more simply: companies are essentially selling inference—that is, computational power packaged in tokens. Behind each model response stand data centers, GPUs, electricity, and architectural constraints. While LLMs are perceived as a subscription or convenient API, this is less noticeable.

But the more workflows depend on a model, the more it begins to resemble a utility resource: with tariffs, priorities, access restrictions, and sensitivity to price. This gives rise to the article's key analogy: LLMs become for intellectual work what electricity became for physical labor. They externalize part of the cognitive load and transform it into a service that can be metered, tariffed, and embedded in business processes.

For companies, this changes their approach to model selection: now it's not only the quality of responses that matters, but also stability, availability, latency, scalability potential, and vendor lock-in.

Token Economics

If we accept this framework, the differences between chats, corporate plans, APIs, and agentic modes become less fundamental. At the base lies one product: access to tokens of a certain quality at a certain price. It is around this unit that the future LLM economy is built.

• computational cost and energy consumption
• alternative returns from GPU and data centers
• market competition and price dumping
• government subsidies and geopolitical support

The author particularly emphasizes that token price cannot fall indefinitely: hardware and energy cost money, and capacity can be directed to other tasks. On the other hand, competition and government support prevent the market from rising freely. The article provides examples of China with aggressive infrastructure subsidization, the US with indirect support for major cloud players through industrial programs, and Europe, which is betting on strategic developers like Mistral.

"The market will need not the best thinking, but sufficiently good

thinking with predictable economics."

This is an important shift: it will not necessarily be the most impressive models that win, but those who can deliver acceptable results cheaper, more reliably, and at scale. The market increasingly resembles an infrastructure market, where outcomes are determined not just by benchmarks, but by resource base, tariffs, and the ability to withstand price pressure.

Price and Control

One of the strongest ideas in the text is that LLMs for the first time make thinking measurable as an operational resource. Previously, the cost of intellectual labor was hidden within specialist hours and project budgets. Now companies can roughly but practically calculate the price of analysis, generation, summarization, option evaluation, and agentic workflows in tokens, latency, and dollars.

Because of this, business begins to design not only processes but also thinking depth: where a cheap model suffices, where deeper reasoning is needed, and where human involvement is mandatory. From this same logic comes a more sober view of model progress. Even if LLMs continue to improve, not every next leap will be noticeable to the average user.

The market may enter a phase where value is created not only by new levels of intelligence, but also by reliability, security, cost reduction, and good integration into real scenarios. Simply put, users will increasingly care not that a model got slightly smarter, but that it became more predictable in operation.

The most vulnerable point of this system is the transition from response to action. While the model simply writes text or searches for information, the cost of an error is limited. But as soon as it is given access to email, documents, CRM, payments, or internal services, it becomes part of the decision-making chain. Here, center stage is taken not only by model quality but by its controllability: protection from prompt injection, rights separation, action verification, and data security. Therefore, for true agency, the market needs not just smart, but managed infrastructure for thinking.

What This Means

The SENSE thesis boils down to a simple conclusion: LLMs are rapidly moving out of the category of spectacular services and becoming a basic resource for intellectual work. This means winners will be products that sell not the magic of demos, but predictable access to thinking—at clear pricing, with risk control, and the ability to integrate into everyday processes.