AI Economics minus the guesswork

When the Harvard Business Review writes, executive boards take notice. So the recent article ‘How to Respond to the Coming AI Cost Shock’ (sorry, we know it's paywalled!) is perhaps the wake up call CFOs globally need to respond to the new metrics of AI Economics.

Token spend is out of control. We got here thanks to subsidised AI pricing, the loss-leading tactic today’s frontier models have used to build market share during the frenzied adoption of AI. The move to today’s more measured, usage-based pricing, will be brutal for the unprepared.

Already early adopters of AI chatbots, image creators and coding tools are now bumping up against token limits every day. How these tokens are paid for and how far off this is from traditional accounting practices is a hot debate. It is time to govern this free-for-all.

HBR author, Stacia Garr, calls out the management challenge. “As organizations replace fixed labor costs with variable AI consumption costs, leaders must stop treating AI as a software purchase and instead view it as an organizational design challenge requiring new approaches to budgeting, workforce planning, and risk management.”

For the last four years, AI vendors built huge valuations and used creative financing to subsidise and obscure the true business cost of tokens. This bullish capital market success story has created a false sense of budget predictability around the building blocks of AI compute.

Many lines of businesses have created their own mini ‘AI projects’ hidden from the view of IT procurement models. Now though AI platforms are seeking to pass on their rising costs to customers, many of whom are hooked on tokens. To add to the confusion around variable costs and actual business value, the rise of viable ‘free’, or open source, options has created a Wild West of AI Economics.

As Garr points out, “Determining how many AI tokens to purchase is useless in the abstract. So is setting an overall AI budget.” she adds “Variable token pricing, fluctuating interaction volume, and unpredictable unit rates make static forecasting impossible. To prevent runaway costs without throttling innovation, enterprises need real-time operational visibility, granular tracking and automated governance across every model call and agent interaction.”

Managing this economic reality requires aligning AI token expenditure directly with human workforce strategy. This is exactly the challenge Yarken was created to solve. Our AI-native approach factors in the complex interplay between token spending and business value which can be demonstrated by a CFO in general ledger.

The HBR article points out “To make all this work, companies must stop viewing AI tokens as an IT line item. Instead, treat all these tokens as a direct alternative to human headcount, but then factor in extra costs to pay for all that change management and potential knowledge loss.”

Yarken’s AI Economics platform bridges this gap—giving enterprise leaders real-time token budgeting, cost-per-outcome modeling, and automated spending guardrails. For CFOs and their team, treating AI consumption with the same financial rigor as workforce management and financial assets is the way forward.