Five-Year AI Cost Model Structure
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.
Where this goes wrong
The failure modes below are the ones worth checking for first. Each describes a way this artefact stops doing its job while still appearing to be in use.
- The model counts build and ignores the run. Inference, storage, monitoring, retraining, and the staff who operate the thing usually exceed the build cost within two years, and they arrive in a different budget line than the one that approved the project.
- Human review is left out. Systems that need a person checking borderline cases carry a permanent staffing cost proportional to volume. Omitting it produces a business case that only works if the oversight is quietly dropped.
- Vendor pricing is treated as fixed for five years. Per-token, per-seat, and per-call pricing all move, and the switching cost is rarely modelled. Include what leaving would cost as a line of its own.
- Decommissioning is absent. Every system ends. A five-year model with no exit cost understates the commitment and makes the cheap-to-start option look better than it is.
Common questions
What does a national AI compute programme actually cost?
The model must carry capital lines for facility, power distribution, cooling, compute and network hardware and initial software licensing, and annual operating lines for power, staffing, hardware refresh cycles, security operations, and the capability still purchased externally. The standard failure is a capital budget with no operating budget behind it: announcements are funded easily, year four is not.