From simple prompts to complex loops
Most organisations begin their AI journey with simple interactions: a prompt goes in, an answer comes out. Behind the scenes, this interaction is measured in tokens — small units of text that determine how much computation is required and, ultimately, how much the request costs.
The shift happens when AI systems stop being single interactions and start becoming processes. In agentic loops, the model does not just respond once — it thinks, calls tools, evaluates results, and iterates. What was once a few thousand tokens can quickly become tens or even hundreds of thousands for a single task.
This is where many AI projects quietly become expensive.
The invisible multiplier: energy consumption
Token usage is only part of the story. The choice of model can amplify impact even further. Recent analyses, such as AI Energy Score v2, show that reasoning models can consume 150 to 700 times more energy than smaller baseline models when performing comparable tasks.
This creates a hidden multiplier effect. A poorly chosen model, combined with inefficient looping, does not just increase costs — it significantly increases environmental impact. In large-scale systems, this difference is no longer marginal; it becomes a strategic concern.
Architecture is where efficiency is decided
The good news is that these outcomes are not inevitable. The economics of AI are largely determined at the architecture level.
Decisions such as which model is used for which task, how much context is passed between steps, and how loops are structured all have a direct impact on both cost and energy use. Small design choices compound quickly.
Efficient systems are not built by accident. They are designed with intent: lightweight models where possible, constrained context windows, caching strategies, and clear termination logic that prevents unnecessary iterations.
From cost control to competitive advantage
Organisations that understand token economics early gain a significant advantage. They are able to scale AI without unexpected cost spikes and without creating unnecessary environmental burden.
More importantly, they can make informed trade-offs: when higher reasoning power is justified, and when simpler approaches deliver the same outcome more efficiently.
This shifts AI from experimentation to sustainable capability.
Turning insight into practice
At Trail Openers, we treat token economics as a core part of every AI initiative. Each pilot begins with a clear baseline: how many tokens are used, where they are consumed, and how that translates into both cost and energy impact.
From there, we identify optimisation opportunities and redesign architectures where needed — not just to reduce cost, but to build systems that can scale responsibly over time.
The result is not only a more efficient system, but a more predictable and sustainable one.