OpenAI and NVIDIA: The End of Free AI Amid Rising Costs

The End of the Illusion of Free AI

For two years, artificial intelligence has been perceived as an abundant and accessible resource. Chatbots, low-cost APIs, and on-demand content generation have contributed to this perception. However, this period is coming to an end as the cost of compute begins to weigh heavily on the entire sector.

Exponential Demand Against Limited Infrastructure

The use of AI models is experiencing explosive growth. At OpenAI, the consumption of tokens via API surged from 6 billion to 15 billion per minute in just a few months. This increase is not merely a rise in the number of users but reflects a shift in usage.

Now, AI is being called upon to orchestrate complete tasks through autonomous agents, such as code generation, workflow automation, and interaction with third-party systems. These uses intensify resource consumption, with each agent potentially requiring several dozen times more compute than a traditional chatbot.

This evolution occurs while infrastructure capabilities remain rigid. The construction of data centers, access to energy, and the production of semiconductors impose non-negotiable timelines, creating an imbalance between supply and demand.

Rising Prices and the Economy of Scarcity

Market tensions are multiplying, leading to a rapid increase in rental prices for GPUs, which are essential for AI computation. The latest generations of NVIDIA chips have seen their hourly costs rise by nearly 50% in just a few weeks on the spot market.

Infrastructure providers, such as CoreWeave, are adjusting their strategies by raising their prices by over 20% and imposing multi-year contractual commitments. For companies reliant on AI, compute is no longer a flexible commodity but a resource to secure.

Simultaneously, AI players like OpenAI are reassessing their priorities. OpenAI has suspended certain developments, particularly in video generation, to focus its capabilities on uses deemed more critical, such as code or enterprise applications.

Towards a New Discipline of Usage

The era of subsidized AI is coming to an end. The ecosystem has largely subsidized usage, with low-cost or even free models to accelerate adoption and capture market share. The token, a unit of measurement for AI consumption, is becoming a true economic unit. As uses become more complex, the bill increases, and the proliferation of agents exacerbates this phenomenon by transforming AI into an active system, continuously consuming compute.

In light of this reality, companies must rethink their approach to AI consumption. This involves prioritizing high-value use cases, optimizing queries and architectures, and diversifying suppliers to mitigate risk.

Service Quality Under Pressure

Capacity tensions are also reflected in a degradation of service. At Anthropic, interruptions are increasing, with availability rates falling below the usual standards of SaaS. Some client companies have already begun to arbitrate between providers to ensure the continuity of their services.

This point is crucial, as AI becomes a critical layer of information systems, yet still does not offer the reliability guarantees necessary for industrial deployment. The gap between technological promise and infrastructural maturity remains significant to this day.

An Industry in Transition

Beyond the cyclical tensions, it is the very nature of the market that is evolving. Artificial intelligence is not just a software product; it relies on heavy infrastructure, combining data centers, energy, and advanced components, whose availability and prices can vary significantly.

This transformation brings AI closer to industries historically constrained by their resources, where production capacity determines growth. In this model, competitive advantage no longer lies solely in the quality of models but in access to compute.

Conclusion

The era of abundant AI is reaching its limits. The rise in usage, combined with the physical constraints of infrastructure, is giving rise to an economy of scarcity. In this new context, compute becomes the central resource, and its cost, the key variable.

The promise of accessible intelligence for all remains. However, it will now have to contend with a simpler reality: producing intelligence comes at a price, and to date, that price is on the rise. The increase in prices is becoming difficult to avoid, but it places players in a delicate situation, as raising rates risks slowing adoption, even as competition remains intense.