If your team cannot articulate the specific business outcome of their AI usage in a single sentence, you don't have an AI strategy. You simply have 'token maxing'—usage for the sake of usage. This framework forces a direct link between AI spend and business results.

After initial unrestricted spending led to budget overruns at companies like Uber, major enterprises are shifting focus. They are moving away from measuring raw AI usage (tokens) and toward implementing AI only for proven use cases with clear ROI, which may benefit cheaper, open-source models over expensive frontier ones.

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.

The AI market has cleared its first ROI hurdle: model revenue has justified massive infrastructure investment. Now it faces a second, harder test. Enterprises spending billions on AI tokens must demonstrate tangible financial benefits, like higher margins or revenue, to sustain the flywheel.

According to Mike Cannon-Brookes, advanced enterprises are not tracking AI success by counting tokens. Instead, they are asking harder questions about overall output, such as engineering productivity and quality. They understand that high token usage doesn't always correlate with high productivity, shifting focus from raw usage to tangible business outcomes.

As companies spend billions on tokens, they will demand justification, similar to how law firms use the billable hour. Vertical AI startups can win by demonstrating the specific ROI of every token used for a business task, answering the question: 'Where's my ROI?'

When selling an AI platform to a CFO, go beyond abstract productivity gains. Calculate the direct cost savings from reducing token consumption on other, less efficient LLMs. This creates a powerful, easily quantifiable business case based on reducing existing AI spend, which resonates strongly with financial leaders.

Categorize all AI token spend into three buckets: 'Tokens that Teach' (valuable experimentation), 'Tokens that Produce' (work output), and 'Tokens that Spin' (wasteful, idle processes). The optimal strategy is to aggressively eliminate 'spin' tokens, optimize 'produce' tokens, and fiercely protect the budget for 'teach' tokens to foster innovation.

Tech companies are shifting from a 'token maxing' mindset—using AI tools indiscriminately—to 'token min-maxing.' This borrows from gaming strategy, focusing on achieving the highest output for the lowest resource cost. It marks a maturation from hype-driven consumption to a more structured, ROI-focused approach with budgets and controls.