As generative artificial intelligence models gain performance and accessibility, the question of How to structure an economy of AI actually viable in the medium term.
Behind the announcements of announcement and the growth of use, hides the more complex reality of still unstable economic models, fragile gross margins, and an increasing tension between lower technical costs and business value requirement.
The cost of the token, a false problem?
In 18 months, the unit inference cost, That is to say the price to generate a word or a token, has dropped spectacularly, some estimate up to 99 %. This drop is the fruit of successive optimizations such as better compression of models, improving architectures, the drop in the price of GPUs for rental.
But for many entrepreneurs, this reduction does not automatically translate into a Improvement of raw margins. In question the need to maintain a high level of service (real time, redundancy, personalization), high orchestration costs, and a value perceived by the endless USER.
Technical pricing in strategic pricing
The dominant economic logic in AI remains largely Input-Driven Incorporating the cost of the API, the volume of tokens, the calculation load. But this approach quickly becomes limiting for IA tool publishers, especially in B2B environments.
Why not Go from the sale of a tool for the sale of a result? This shift would switch from an IT budget to an operational budget, better valued in the organization and less exposed to short -term arbitration.
This pricing trend is visible in certain vertical platforms, such as Harvey in the legal or healthy health sector, which charge a business transformation rather than technological access.
Grute margin: do not be satisfied with an instantaneous
It is tempting to judge an AI startup in terms of its current gross margin. But this isolated data can be misleading. What matters, according to long -term investors, is The trajectory towards a consolidated and scalable margin.
In other words:
-
- Can we demonstrate that the marginal cost decreases as the use increases?
- Is the company capable of creating a differentiation that justifies a premium pricing?
- Is there a learning loop (product or market) which strengthens value over time?
Without an answer to these questions, even a promising product may remain in a gray area between attractive technology and uncertain commercial viability.
The perceived value, sine qua non condition of a lasting model
The key to a sustainable economy does not only reside in cost control and is played in the ability to generate clear, stable, and differentiating perceived value For the end customer.
However, this value is still difficult to objectify in many cases. The promised productivity gains are struggling to translate into concrete operational indicators. The generic character of the models makes differentiation difficult. And adoption, often rapid at the start, stagnates when it is not anchored in real business use.
This is why some publishers now guide their offer to Specific segmentswith suitable language, advanced integration and commitment to results. This more artisanal approach sometimes limits the immediate scale, but Better anchor value and retention.
Between declining costs and increasing expectations, a new balance to invent
AI economy cannot be satisfied with a favorable technological equation. To become lasting, she will have to be articulated with Mastered unit costs, a credible margin trajectory, and a perceived value which exceeds the technical demonstration.
