Five-Year AI Cost Model Structure

Last updated: 2026-08-14

A five-year AI cost model must carry capital lines covering facility, power distribution, cooling, compute and network hardware and initial licensing, and annual operating lines covering power, staffing at market rates, hardware refresh, security operations, and the capability still bought externally. Understating any line is the standard failure.

The most common failure in sovereign AI programmes is a capital budget with no operating budget behind it. Announcements are funded easily; year four is not.

The template

Build the model with these lines. Understating any of them is the standard failure.

Capital. Facility, power distribution, cooling, compute hardware, network hardware, initial software licensing.

Operating, annual. Power at realistic utilisation. Cooling. Hardware refresh provision at 20 to 33 percent of compute capital per year. Software and support licensing. Connectivity. Facility operations.

People, annual. Platform engineering, security operations, data engineering, model operations, governance function. Cost these at retention-competitive rates, not at standard public scale, then state the gap explicitly rather than hiding it.

Programme, annual. Evaluation and benchmarking, corpus and dataset development, training and capability building, external assurance.

Contingency. Not less than 15 percent. Power costs and hardware availability have both moved sharply and will again.

How to use it

• Build every line, including the ones that make the total uncomfortable. An omitted line does not reduce the cost, it defers the discovery.
• Cost staffing at market rates rather than public-sector scales, or the model assumes people who will not be hired.
• Include the capability you will still buy externally. Sovereign infrastructure rarely removes all external spend.
• If the honest total is unaffordable, hold fewer layers well rather than every layer badly.

This template comes from the Sovereign AI Strategy: A Practical Playbook, which sets out the reasoning behind it, the rules of thumb that govern its use, and the signals that tell you the approach is failing. The full playbook is also available as Markdown. See all AI governance templates.