Plummeting token prices never cut AI budgets; they let teams burn more. Because modern models ship with reasoning on by default, "thinking" eats many times more tokens before the answer arrives, a textbook Jevons paradox. AI spend keeps climbing even as cost per million tokens falls, so marketers should match model size to task complexity and price on cost-per-correct-answer, not raw token rates.
A decade of explosive growth pushed the martech landscape past 15,000 tools, but that abundance is now the problem: duplicative apps, fractured data, and runaway integration costs make more tech mean worse operations. AI is the accelerator, folding content, analytics, personalization, and orchestration into single platforms. The real question for leaders is no longer "can a tool do this?" but whether a capability earns its place as another layer of complexity.
Autonomous marketing agents sell themselves on killing manual labor, but their cost model is wildly different from fixed per-seat SaaS. Every task burns tokens and API calls, and background loops that parse live databases add variable spend nobody budgeted for. The piece walks MOps leaders through modeling true TCO, from forecasting token volume to pricing the middleware engineering and QA required to glue agents to CRMs and ad networks. Get the math right before rollout, or your AI workforce quietly outspends the humans it replaced.