While the cost-per-token is decreasing as models become more efficient, this efficiency gain drives a massive increase in new use cases and overall consumption. This economic principle, Jevons Paradox, explains why total enterprise spending on model inference is skyrocketing, even as the unit cost falls.

Incentivizing high AI token usage is not waste, but a form of R&D. In the new agentic paradigm, there are no best practices. Mass experimentation, even with failures, is the only way to discover future workflows and avoid being left behind.

The most heated topic among Fortune 500 CIOs is no longer which AI model is most powerful, but how to manage unpredictable and soaring token costs. Companies are struggling to find the right strategies—from workload prioritization to user-based access tiers—to create a predictable cost model in a rapidly evolving tech landscape.

Fears of revenue collapse from companies optimizing token usage are premature. While top firms implement spending caps, the median company spends a trivial $11.38 per employee on AI. The massive growth potential as these firms scale their usage will dwarf any revenue lost at the top end from efficiency-seeking behavior.

A paradox exists where the cost for a fixed level of AI capability (e.g., GPT-4 level) has dropped 100-1000x. However, overall enterprise spend is increasing because applications now use frontier models with massive contexts and multi-step agentic workflows, creating huge multipliers on token usage that drive up total costs.

High token consumption is framed as a key metric for AI leverage, not a cost. This goal forces teams to find ways to delegate more complex, long-running, and parallel tasks to AI agents, thus maximizing the intelligence and autonomous work extracted from the models.

In the AI era, token consumption is the new R&D burn rate. Like Uber spending on subsidies, startups should aggressively spend on powerful models to accelerate development, viewing it as a competitive advantage rather than a cost to be minimized.

The trend of companies like Uber and Meta capping employee AI usage, dubbed "token panic," does not signal a decline in overall AI demand. Instead, it marks a critical market shift towards prioritizing cost-effectiveness, creating a strong business imperative for more token-efficient models and applications.

The massive growth in AI token consumption isn't a sign of waste but of ambition. While the cost per "unit of intelligence" is decreasing, companies are immediately applying that efficiency to solve exponentially harder problems. Our appetite for more capable AI is growing faster than the cost is falling, leading to sustained, exponential spending.