Quel modèle économique l’IA impose-t-elle aux startups ?

AI and Startups: Economic Model Challenges

As generative AI models advance in performance and accessibility, the question arises of how to structure a viable AI economy in the medium term.

The increasing use of these technologies comes with challenges related to unstable economic models, fragile gross margins, and a growing tension between decreasing technical costs and the demand for business value.

Over the past 18 months, the unit cost of inference—generating a word or token—has dramatically decreased, with some estimates as high as 99%. This reduction is due to continuous optimizations, including better model compression, improved architectures, and reduced GPU rental prices.

However, many entrepreneurs do not see this reduction immediately improving their gross margins due to the need for high service levels (real-time, redundancy, customization), high orchestration costs, and uncertain perceived value by the end-user.

The prevailing economic logic in AI is largely input-driven, considering API costs, token volume, and computational load. This approach can be limiting for AI tool developers, especially in B2B environments.

Transitioning from selling a tool to selling a result could shift the budget from IT to operational, better valued within organizations and less susceptible to short-term cuts.

This pricing trend is evident in some vertical platforms, such as Harvey in the legal field or OpenEvidence in healthcare, which charge for business transformation rather than technological access.

Evaluating an AI startup based solely on its current gross margin can be misleading. What matters, according to long-term investors, is the trajectory towards a consolidated and scalable margin.

Can the company demonstrate that marginal costs decrease as usage increases? Is the company capable of creating differentiation that justifies premium pricing? Is there a learning loop (product or market) that reinforces value over time?

Without answers to these questions, even a promising product risks remaining in a gray area between appealing technology and uncertain commercial viability.

Perceived Value: Essential for a Sustainable Model

The key to a sustainable AI economy lies not only in cost control but also in the ability to generate clear, stable, and differentiating perceived value for the end customer.

This value is often difficult to quantify. Promised productivity gains struggle to translate into concrete operational indicators. The generic nature of models makes differentiation challenging, and adoption often stagnates without real business application.

Some developers are now targeting specific segments with tailored language, deep integration, and a commitment to results. While this approach may limit immediate scale, it better anchors value and retention.

The AI economy cannot rely solely on favorable technological equations. To become sustainable, it must align with controlled unit costs, a credible margin trajectory, and perceived value that goes beyond technical demonstration.

D’après FrenchWeb.