转向代币经济学：驾驭AI的新支出动态

A note from the authors: AI economics affect mostorganizations and the C-suite uniquely.This paper guides those familiarwith AI tokens in making strategicchoices.If youʼre just beginning yourexploration of tokenomics, look foradditional research soon. Traditional total-cost-of-ownership frameworksmissthe realityof AI Volatile workloads, newinfrastructure demands,and tokens as thepractical unit of cost algorithmic complexity, and infrastructure intensity. What exactlyare the thresholds to move across different consumption choices?It depends on the organization. Roughly a quarter of respondentsin a Deloitte 2025 survey4of data center and power executivessay they or their clients are ready to make the move off of cloudto alternatives as soon as costs reach just 26% to 50% of thosealternatives, showing high sensitivity to even modest price changes,while others plan to wait until cloud costs exceed 150% of thecost of alternatives. The decision point remains unclear given thehigh variability patterns of AI technologies. For example, advancedreasoning models that keep context across multiple steps canconsume much more compute than basic one-shot responses.As NVIDIA projects a billion-fold surge in AI computingandGooglenow processes 1.3 quadrillion tokens a month5—a 130-fold leap injust a year—the capital and energy implications are profound. Across industries, Generative AI (GenAI) has become the fastest-growing line item in most corporate technology budgets—alreadyconsuming up to half of IT spend in some firms.1Cloud bills are risingnearly 20% year over year, driven by AI workloads.2At the same time,geopolitical uncertainties are intensifying calls for data sovereigntyand technology infrastructure independence, making manyenterprises think about AI sovereignty and gaining greater controlover their infrastructure.3This is no longer a CIO operational issue;it is a CFO-and-board capital question about how to responsiblymanage an investment of this scale and volatility. Traditional total cost of ownership (TCO) approaches are no longerthe best way to manage AI economics. Leaders may be better servedby precision economics—the ability to track, predict, and optimizespend at the token level. Tokens translate opaque infrastructurechoices into tangible financial terms: the true cost of generatinga dollar of revenue, margin, or productivity. Unlike prior technology waves governed by licenses or virtualmachines, AI spend often scales in nonlinear and unpredictableways. AI capabilities run ontokens: small chunks of data—text,image or audio—that AI systems process in training, inference,and reasoning. Every AI interaction consumes tokens, and everytoken carries a cost. The competitive divide will not likely hinge on who adopts AI first, buton who manages its cost structure with discipline. AI spend will likelyseparate value creators from value eroders. The former converttokens into measurable enterprise output; the latter accumulateungoverned cost that compounds quietly across the stack. The complexity of AI’s economics hides within these tokens.Costs rise not only with user adoption but with workload design, The elusive AI ROI Despite rising investment, many leaders appear to stillbe chasing measurable return on investment (ROI) fromAI initiatives. •Nearly half (45%) of 500 leaderssurveyed in Deloitte’s 2025 USTech Value surveyexpect it will takeup to three years to see return oninvestment from basic AI automation.6 •Six in 10 of those completing Deloitte’s2025 Tech Value survey believe moreadvanced AI automation will take evenlonger to reach ROI. •Of the 1,326 global finance leaderssurveyed for Deloitte Global’s inauguralFinance Trends report,fielded May2025, 28% said AI investments aredelivering clear, measurable value.7 But the issue isn’t whether AI will deliver value—it’show to measure and manage that value in a way ROIframeworks cannot. For many organizations, adoptingAI is no longer optional; it’s a strategic response tocompetitive or existential pressure. That makes understandingtheeconomics of AI—how costs, workloadsand returns flow throughtokens—thenewimperative for leaders. Tokens:The newcurrency of AI Unlike traditional pricing based on compute time—which is relatively static—token-basedpricing ties cost directly to the actual work AI performs.Each token represents botha unit of computation and a unit of cost.In that sense, tokens are thetrue currencyof AI economics—as indispensable to machine intelligence as kilowatt hours are toelectricity. The difference is that token demand is far harder to predict or control, makingAI spend inherently volatile. •Nonlinear demand:Complex reasoning models improve performancebut can consume more tokens than simple inference tasks.•Fluctuating token use:Token use fluctuates with experimentationlevels, workload design, model choice and even prompt engineering.•Varying pricing:Token price keeps changing based on AI modelcapabilities and the efficiency of the und