The end of cheap AI: the token shortage has begun

The AI industry is undergoing a fundamental shift away from the era of subsidised, cheap access to powerful models. As reported in the AI Daily Brief's May 2026 recap, we are now entering a period defined by token scarcity and usage-based pricing. For businesses that have grown accustomed to low-cost experimentation, this signals an urgent need to focus on efficiency, cost management, and return on investment for all AI initiatives.

The AI Daily Brief's latest analysis concludes that May 2026 was a pivotal month, marking the end of the AI 'subsidy era' and the beginning of a new phase driven by resource constraints.

The subsidy era is over

For the last few years, major AI labs have been engaged in a fierce battle for market share and user adoption. A key strategy was to heavily subsidise the cost of using their large language models (LLMs). This made powerful AI accessible to millions of businesses and developers, often for free or at a fraction of its true cost. This period encouraged widespread experimentation but masked the immense computational expense required to generate AI responses.

Now, the economic reality is setting in. The demand for AI compute is beginning to outstrip the available supply, leading to a shift in pricing strategy from model providers.

Understanding the 'token shortage'

A 'token' is the basic unit of text that an AI model processes, roughly equivalent to a word or part of a word. Every request you send to an AI and every response it generates consumes tokens. The 'shortage' is not that we are running out of tokens themselves, but rather that the computational resources needed to generate them are finite and increasingly expensive.

This economic scarcity is manifesting in several ways:

This shift from subsidised access to real-cost pricing has significant implications for any business using or planning to use agentic AI. The focus must now pivot from broad experimentation to disciplined, value-driven implementation.

From experimentation to demonstrable ROI

When AI was cheap or free, the business case for any given application could be loose. Now, every API call has a direct, measurable cost. This forces a necessary discipline: Is this AI-powered workflow generating more value than it costs to run?

For small businesses, this means being highly selective about which AI tools to adopt and paying close attention to subscription tiers and usage limits. For larger enterprises, it necessitates a robust governance framework to prevent runaway costs from decentralised AI usage across different teams.

Budgeting for AI is now non-negotiable

Treating AI as an unmonitored operational expense is no longer viable. Businesses must now budget for AI compute as they would for any other critical utility like electricity or cloud hosting.

This involves:

• Forecasting: Estimating token consumption for planned agentic workflows.
• Monitoring: Implementing dashboards to track real-time AI usage and costs.
• Attribution: Assigning AI costs to specific departments, projects, or clients to accurately measure profitability.

Efficiency is the new competitive advantage

The organisations that thrive in this new era will be those that can achieve their goals with the greatest token efficiency. It's not just about having the most powerful model, but about using the right model for the job in the most optimal way.

This creates opportunities for businesses that invest in:

• Workflow Redesign: Re-engineering processes to minimise unnecessary AI interactions.
• Prompt Engineering: Crafting queries that get the desired result with the fewest tokens.
• Model Selection: Using smaller, faster, and cheaper models (including open-source alternatives) for tasks that don't require a top-tier frontier model.

Navigating the end of the AI subsidy era requires a proactive and strategic approach. Business operators should not view this as a barrier, but as a prompt to build more sustainable and valuable AI capabilities.

1. Conduct an AI Usage Audit. Before you can manage costs, you need visibility. Identify all AI tools, APIs, and platforms currently in use across your organisation. Who is using them, for what purpose, and what is the current cost?

2. Educate Your Team on Token Economics. Ensure that key personnel, from developers to project managers, understand what tokens are and how their decisions impact overall AI expenditure. A simple dashboard showing usage can be a powerful educational tool.

3. Prioritise High-Value, High-ROI Use Cases. Analyse your pilot projects and existing workflows. Double down on the applications that provide the clearest and most significant return on investment. Defer or halt projects where the value is ambiguous or the token cost is prohibitive.

4. Develop a Model-Tiering Strategy. Not every task requires GPT-5. Work with your technical team to create a strategy for using different models based on task complexity. Use powerful, expensive models for complex reasoning and cheaper, faster models for summarisation, data extraction, or simple classification.

5. Implement Cost Controls and Alerts. Use the tools provided by your cloud or AI vendor to set hard budget limits and alerts. This prevents 'sticker shock' and ensures that you stay within your forecasted AI spend.