To combat engineer skepticism, companies incentivized AI usage to the point of wastefulness ("token maxing"). This overcorrection is a faster way to achieve broad adoption than starting with strict controls. You can enforce responsible usage and cost efficiency once the value is proven and ingrained.

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.

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.

Aggressive token budgeting prevents employees from experimenting and discovering new AI-native workflows. Companies that overly restrict usage will not see the productivity gains needed to reshape their business and will ultimately be outcompeted by those that encourage more liberal use.

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.

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.

While large enterprises must constrain AI model usage to control costs, startups should embrace 'token-maxxing.' By giving developers unfettered access to the most powerful models, startups gain a crucial productivity and talent-attraction advantage over larger, more bureaucratic competitors.

Even as enterprises optimize AI spending for better ROI, overall spend will continue to grow rapidly. The adoption curve for new use cases and new enterprises is so steep that it overwhelms any efficiency gains from optimization, ensuring continued growth for model providers.

For hyper-growth companies, the cost of losing a competitive edge by not adopting powerful AI tools far outweighs the direct token costs. The opportunity cost of inaction makes any efficiency gain worth the price.