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.

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.

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.

The current subsidized AI subscription model is unsustainable. The inevitable shift to pay-per-token pricing will expose the true cost of inference. For tasks like coding, where AI can "hallucinate" and burn tokens in loops, this creates unpredictable and potentially exorbitant costs, akin to gambling.

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.

The current model of paying per AI token is a temporary phase. Drawing a parallel to computing history, any resource constraint that requires payment eventually moves to the user's local device and becomes free. On-device AI processing will follow this pattern, ultimately eliminating token costs.

The cost of AI, priced in "tokens by the drink," is falling dramatically. All inputs are on a downward cost curve, leading to a hyper-deflationary effect on the price of intelligence. This, in turn, fuels massive demand elasticity as more use cases become economically viable.

The massive spike in demand for AI tokens is a direct result of the shift from users performing simple, assisted tasks to deploying autonomous agents. A single individual can now consume billions of tokens via agents running on their behalf, overwhelming the current supply of compute.

The primary short-term risk for the AI sector isn't capital expenditure but the high cost of token generation. For AI applications to become ubiquitous, the unit economics must improve. If running a single query remains prohibitively expensive for businesses, widespread, sustainable adoption will be impossible, threatening the entire investment thesis.