The current affordability of AI tokens is not sustainable; it's propped up by venture capital funding AI companies operating at a loss. Businesses should treat this as a temporary window for aggressive learning and experimentation before prices inevitably rise to reflect true operational costs.
Encouraging high AI token usage ('token maxing') becomes actively harmful when an employee lacks fundamental skills. They use expensive tools to produce poor work faster, amplifying their negative impact instead of driving positive outcomes. This is a significant hidden risk in broad AI adoption.
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.
A core part of a real AI strategy is creating repeatable actions, not just completing one-off tasks. Before starting an AI project, apply a simple filter: 'Will I use this more than once?' If the output is completely disposable and takes significant time, it's likely not a strategic use of resources.
Unlimited access to AI tools often results in wasted time on frivolous or bad ideas. Similar to how SNL's Lorne Michaels edits creatives to prevent them from 'getting in their own way,' managers must impose constraints and structure to guide AI usage toward valuable outcomes.