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 trend of "token maxing"—unrestrained spending on AI usage—is being corrected. Companies like Meta are realizing that, like any business expense, AI token consumption must be "min-maxed": optimizing for the highest leverage output at the lowest possible cost, not just maximizing usage.

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.

Current unprofitability in some AI applications, like subsidizing tokens for coding, is a deliberate strategy. Similar to Uber's early city-by-city expansion, AI labs are subsidizing usage to rapidly gain market share, gather data, and build a powerful flywheel effect that will serve as a long-term competitive moat.

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 smartest 'AI-pilled' companies adopt a two-tiered model strategy. They use expensive, frontier models for internal, high-leverage tasks like creating new knowledge and optimizing processes. However, they use cheaper, open-weight models in the 'bill of materials' for the customer-facing product to manage costs effectively.

Big tech companies are offering their most advanced AI models via a "tokens by the drink" pricing model. This is incredible for startups, as it provides access to the world's most magical technology on a usage basis, allowing them to get started and scale without massive upfront capital investment.

Despite fears of runaway costs from "token maxing," enterprises are overwhelmingly encouraging more AI model consumption. A developer survey found 7x more companies were told to increase spending. The value gained from experimenting on AI's rapidly expanding capability frontier currently outweighs the push for cost optimization.