How token economics and cost is limiting AI adoption

Token usage has emerged as a significant operating cost for enterprises, shifting from experimental use to scaled deployment [1]. Because AI consumption is directly tied to compute spend, these costs are inflating budgets and creating economic friction that hinders broader adoption [1, 5, 9]. In many instances, the cost of tokens makes AI more expensive than human labor, with studies indicating that AI is only more cost-effective than humans in 23% of tasks [5, 6]. Furthermore, the industry faces challenges as agentic workloads consume significantly more tokens per task, often colliding with existing flat-rate subscription models [4]. As companies look toward IPOs, there is also concern that the current era of low-cost AI may end, as firms move away from subsidized pricing to prioritize profitability [8]. Consequently, organizations are finding that they need new budgeting and measurement frameworks to manage these rising expenses effectively [1].