Artificial intelligence is shifting from pilot projects to everyday tools, and with that shift the old world of predictable licences, fixed subscriptions and planned hardware upgrades is starting to crack. Instead of counting users or servers, organisations now find themselves counting tokens, the tiny units of data that power AI models, and discovering that their financial exposure grows as employees embed AI into more workflows. Analysts tracking global trends expect AI spending to surge into the trillions of US dollars by the middle of the decade, which puts fresh pressure on leaders to understand the mechanics behind token-driven usage rather than just the headline promises of automation.
At the centre of this new cost model sits the token, the basic unit an AI model consumes whenever it reads text, analyses an image or reasons through a task. Every prompt, every agent workflow and every background process consumes tokens, and small design choices in prompts or orchestration can multiply that usage. A slightly more verbose system message, an agent that calls another agent for verification or an extra step in a reasoning chain can all quietly increase token volumes. Scaled across hundreds or thousands of agents running continuously, those marginal additions become material spending and tie infrastructure, software choices and model selection directly to the balance sheet.
This dynamic creates a modern version of Jevons’ paradox for AI. As the cost per token falls with better chips and more efficient models, organisations find more ways to use them so total spend still climbs. Internal research from major consultancies suggests that while token prices trend down over time, aggregate enterprise AI outlay keeps rising because adoption widens, use cases deepen and multi-step multi-agent workflows become normal rather than experimental. In other words, cheaper tokens invite heavier use and the overall bill reflects volume more than unit price.
How a company buys AI has a huge influence on this cost curve. Traditional packaged software that embeds AI tends to wrap token usage inside simple subscription tiers, which makes budgeting straightforward but can hide inefficient consumption. API-based AI offers the opposite, with clear per-token pricing and granular usage data but also exposure to volatility when demand spikes or more complex prompts roll out. At the other end of the spectrum some enterprises are building their own “AI factories”, clusters of GPUs, storage and orchestration tools, which demand significant upfront capital but become more economical as token volumes reach into the tens or hundreds of billions each year. At modest scale API access often wins on flexibility and price, while at very high scale in-house infrastructure can end up several times cheaper over the life of the workloads.
Because of this, AI can no longer be governed as just another software rollout, it behaves more like an economic system inside the organisation. Forward-leaning enterprises track tokens the way industrial firms track energy or capital, forecasting usage, setting thresholds and linking token consumption directly to measurable business outcomes. Emerging AI-focused financial operations frameworks extend this discipline across GPU hours, token spend and workload performance so finance, technology and business teams all see the same real-time picture. This transparency makes it easier to implement internal chargebacks, enforce guardrails against silent overspend and steer investment toward the most productive use cases.
Looking ahead, enterprise AI architectures seem likely to be hybrid by default. Many organisations will continue to experiment quickly with public APIs then migrate predictable high-volume workloads onto owned or dedicated infrastructure once they understand the scale. Others will mix general-purpose cloud with specialist GPU providers to balance performance, sovereignty and negotiating power. In this environment leadership teams need to treat AI as a portfolio of economic choices, right-size models so frontier systems are reserved for tasks that truly need them, decide when token volume justifies moving off APIs and embed token-aware governance into every major project.
AI is clearly reshaping enterprise economics and tokens are becoming the new unit that connects technical design with financial outcomes. Organisations that learn to manage token consumption with the same rigour they apply to capital expenditure look likely to innovate faster, scale more effectively and hold a durable edge over rivals. Those that ignore this shift may find their AI bills climbing faster than the value they create as competitors lock in smarter, more disciplined token strategies.
