Contrary to the view that AI token intensity will drop after the initial coding boom, the move from simple queries to autonomous 'agentic' workflows will cause an order-of-magnitude (10x) increase in token usage per task. This applies across all knowledge-based jobs, ensuring sustained and explosive demand for compute.

A contrarian view argues that encouraging high token usage ("token maxing") is a valid short-term strategy. The rationale is that the engineering challenge of building systems capable of consuming tokens at massive scale is a significant achievement and a proxy for deep AI integration, making the raw cost secondary.

To get teams experimenting with AI, leaders should provide an open budget for tokens initially. Being 'profligate' at the start is crucial, as imposing constraints too early leads to unimpressive results, stifles creativity, and hinders true adoption. Efficiency can be optimized later.

Companies like Meta are pushing a new practice called "token maxing," where developers are encouraged to spend heavily on AI coding assistant tokens. This is being gamified with leaderboards to accelerate output, but it raises questions about efficiency versus vanity metrics and whether it's a true indicator of productivity.

Progress in complex, long-running agentic tasks is better measured by tokens consumed rather than raw time. Improving token efficiency, as seen from GPT-5 to 5.1, directly enables more tool calls and actions within a feasible operational budget, unlocking greater capabilities.

In the current 'capability exploration' phase, companies incentivize developers to use as many AI tokens as possible. This serves as a visible, albeit inefficient, signal of AI adoption to management, prioritizing quantity over quality.

Some large companies are incentivizing employees to use the maximum amount of AI tokens, even ranking them on usage. This seemingly inefficient strategy is a deliberate investment to accelerate adoption. The goal is to retrain employee thinking to be "AI native" before optimizing for cost and efficiency.

To foster breakthrough ideas, companies should initially provide engineers with unrestricted access to the most powerful AI models, ignoring costs. Optimization should only happen after an idea proves its value at scale, as early cost-cutting stifles creativity.

Ramp's CPO argues companies shouldn't excessively worry about AI token costs. If an AI agent can deliver 10x the output of a human, it's logical and profitable to pay the agent (via tokens) more than the human's salary. This reframes ROI from a cost center to a massive productivity investment.